11 · Bibliography
The Annotated Bibliography
Scope: The full source registry behind the literature review: every entry resolved to a DOI, publisher record or open-access record, with the verification date carried per source.
Every source cited across the seven literature-review sections, grouped by the section that cites it, with the provenance and verification date carried from the source registry. 66 entries in all.
Section 1 · The Screener Landscape · 17 Sources
Section 2 · From Self-Report To Authentic Voice · 13 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| Bauer, M., & Zapata-Rivera, D. (2020). Cognitive foundations of automated scoring. In D. Yan, A. A. Rupp, & P. W. Foltz (Eds.), Handbook of automated scoring: Theory into practice (pp. 13–28). Chapman and Hall/CRC. | Supports the Brief's argument that construct-clarity and cognitive grounding, not model scale or black-box fit, are what make automated scoring trustworthy, the same argument Ober et al. make from the LLM-coding side in S2. | VERIFIED verified 2026-08-25 |
| Cao, W., Mirjalili, V., & Raschka, S. (2020). Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters, 140, 325–331. doi 10.1016/j.patrec.2020.11.008 | Direct methodological citation for the CORAL ordinal-regression head named in Impacter Pathway's own architecture (DeBERTa-v3-base + CORAL, per the live Model Card). Grounds the 'discriminative ordinal pipeline, not generative sampling' argument made in S2/S3, and the same head named in S7's production-architecture discussion. | VERIFIED verified 2026-08-25 |
| He, P., Gao, J., & Chen, W. (2021). DeBERTaV3: Improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. arXiv:2111.09543. | The architecture-family paper for the DeBERTa-v3-base encoder Impacter Pathway's scoring pipeline is built on, per the live, confirmed Model Card (impacterpathway.com/model-card). Supports the S3 model-facts framing. | VERIFIED verified 2026-08-25 |
| IMPACTER Education Corp. (2026). IMPACTER Model Card, Version 6.0: CORAL Ordinal Regression Models. | The sole authorized source for any model-version accuracy figure appearing in the Brief. | INTERNAL verified 2026-08-25 |
| Fisher, H., Jaffe, N. M., Pidvirny, K., Tierney, A. O., Vaidean, M. S., Dongre, P., & Webb, C. A. (2026). Language-based detection of depression with machine learning: Systematic review and meta-analysis. npj Digital Medicine, 9, Article 273. doi 10.1038/s41746-026-02448-1 | Establishes the published accuracy ceiling for general-purpose language-based depression detection in the clinical NLP literature, useful context for framing a purpose-built, rubric-aligned scoring pipeline as a distinct, narrower measurement task rather than a clinical diagnostic claim. | VERIFIED verified 2026-08-25 |
| Low, D. M., Bentley, K. H., & Ghosh, S. S. (2020). Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investigative Otolaryngology, 5(1), 96-116. doi 10.1002/lio2.354 | Frames the acoustic/paralinguistic branch of automated language-based mental health assessment, distinct from a text-based, rubric-scored approach, and shows how young this broader evidence base still is even for well-studied constructs like depression. | VERIFIED verified 2026-08-25 |
| Teferra, B. G., Rueda, A., Pang, H., Valenzano, R., Samavi, R., Krishnan, S., & Bhat, V. (2024). Screening for depression using natural language processing: Literature review. Interactive Journal of Medical Research, 13, e55067. doi 10.2196/55067 | Names the specific fairness, privacy, and interpretability risks that a multilingual, student-facing scoring system must design against. | VERIFIED verified 2026-08-25 |
| Zapata-Rivera, D., & Forsyth, C. M. (2022). Learner modeling in conversation-based assessment. In Adaptive Instructional Systems: Adaptation Strategies and Methods (HCII 2022), Lecture Notes in Computer Science (Vol. 13332). Springer. doi 10.1007/978-3-031-05887-5_6 | Establishes ETS's conversation-based-assessment lineage as the closest existing large-testing-organization precedent for treating open-ended student language as a scorable measurement signal. | VERIFIED verified 2026-08-25 |
| Lopez, A. A., Guzman-Orth, D., Zapata-Rivera, D., Forsyth, C. M., & Luce, C. (2021). Examining the accuracy of a conversation-based assessment in interpreting English learners' written responses (Research Report No. RR-21-03). ETS Research Report Series. doi 10.1002/ets2.12315 | Direct precedent for automated interpretation of open-ended, English-learner student language in an assessment context, supporting the case that language-based scoring is workable across linguistic backgrounds when the task is well-specified. | VERIFIED verified 2026-08-25 |
| Ober, T. M., Zhang, S., Zapata-Rivera, D., Schroeder, N. L., & Botelho, A. F. (2026). Using LLMs to identify indicators of persistence from students' dialogues with a pedagogical agent. Journal of Educational Data Mining, 18(1), 208-243. doi 10.5281/zenodo.18852441 | Directly supports the argument that rubric-first, operationally precise construct definition is what a generative LLM-coding pipeline needs to be reliable, and by extension, why a discriminative, deterministic, frozen-and-versioned ordinal scoring architecture avoids the reproducibility problem this paper documents. | VERIFIED verified 2026-08-25 |
| Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023). | Documents the class of instability and reproducibility risk inherent in generative, sampling-based LLM reasoning, the same risk category Ober et al. (2026) found in LLM-based construct coding, and the basis for distinguishing Impacter Pathway's deterministic discriminative pipeline from generative LLM coding. | VERIFIED verified 2026-08-25 |
| Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. | Standard reference for transformer model-compression technique; relevant background for understanding tradeoffs between model size and scoring reliability discussed in the encoder-architecture literature this section engages. | VERIFIED verified 2026-08-25 |
| He, P., Liu, X., Gao, J., & Chen, W. (2021). DeBERTa: Decoding-enhanced BERT with disentangled attention. In Proceedings of the 9th International Conference on Learning Representations (ICLR 2021). | Establishes the DeBERTa encoder lineage (disentangled attention over BERT/RoBERTa) that Impacter Pathway's production DeBERTa-v3-base encoder descends from, distinguishing an encoder-plus-ordinal-head architecture from the generative decoder-only LLMs discussed elsewhere in this section. | VERIFIED verified 2026-08-25 |
Section 3 · Trustworthy Automated Scoring · 12 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| Williamson, D. M., Xi, X., & Breyer, F. J. (2012). A framework for evaluation and use of automated scoring of constructed-response tasks. Educational Measurement: Issues and Practice, 31(1), 2–13. doi 10.1111/j.1745-3992.2011.00223.x | Supplies the evaluation vocabulary (agreement, degradation, fairness, consequences) that Impacter Pathway's own promotion-gate framework (QWK ≥ 0.70, degradation ≤ 0.10, |SMD| < 0.10) is built on. | VERIFIED verified 2026-08-25 |
| McCaffrey, D. F., Casabianca, J. M., Ricker-Pedley, K. L., Lawless, R. R., & Wendler, C. (2022). Best practices for constructed-response scoring (Research Report No. RR-22-17). ETS Research Report Series. doi 10.1002/ets2.12358 | Corroborates Impacter Pathway's practice of governing human and automated scoring under one shared quality framework, and supports the Brief's evaluation-standards section with an ETS-sourced best-practices citation. | VERIFIED verified 2026-08-25 |
| Wood, S., Yao, E., Haisfield, L., & Lottridge, S. (2021). Establishing standards of best practice in automated scoring (ACT Research Technical Brief). ACT, Inc. | A second independent testing-industry standards source (alongside Williamson/Xi/Breyer and AERA/APA/NCME) supporting the Brief's evaluation-standards framing; publicly hostable per the copyright rules. | VERIFIED verified 2026-08-25 |
| Bauer, M., & Zapata-Rivera, D. (2020). Cognitive foundations of automated scoring. In D. Yan, A. A. Rupp, & P. W. Foltz (Eds.), Handbook of automated scoring: Theory into practice (pp. 13–28). Chapman and Hall/CRC. | Supports the Brief's argument that construct-clarity and cognitive grounding, not model scale or black-box fit, are what make automated scoring trustworthy, the same argument Ober et al. make from the LLM-coding side in S2. | VERIFIED verified 2026-08-25 |
| American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association. | The highest-authority umbrella standard the Brief can cite to frame Impacter Pathway's evaluation practices as consistent with the field's own professional testing standards. | VERIFIED verified 2026-08-25 |
| Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. doi 10.1177/001316446002000104 | The foundational citation for the agreement-statistic vocabulary (kappa family, including QWK) used throughout Impacter Pathway's scoring-evaluation reporting. | VERIFIED verified 2026-08-25 |
| Krippendorff, K. (2004). Reliability in content analysis: Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433. doi 10.1111/j.1468-2958.2004.tb00738.x | Second agreement-statistic foundation (alongside Cohen 1960) supporting Impacter Pathway's inter-rater and human-machine reliability reporting, especially useful where more than two raters or ordinal scales are involved. | VERIFIED verified 2026-08-25 |
| Cao, W., Mirjalili, V., & Raschka, S. (2020). Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters, 140, 325–331. doi 10.1016/j.patrec.2020.11.008 | Direct methodological citation for the CORAL ordinal-regression head named in Impacter Pathway's own architecture (DeBERTa-v3-base + CORAL, per the live Model Card). Grounds the 'discriminative ordinal pipeline, not generative sampling' argument made in S2/S3, and the same head named in S7's production-architecture discussion. | VERIFIED verified 2026-08-25 |
| Loukina, A., Madnani, N., & Zechner, K. (2019). The many dimensions of algorithmic fairness in educational applications. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2019) (pp. 1–10). Association for Computational Linguistics. doi 10.18653/v1/W19-4401 | Supplies the fuller fairness taxonomy against which Impacter Pathway's own promotion-gate |SMD| < 0.10 subgroup check can be located as one specific (overall-score-difference) fairness test among several, useful for scoping the Brief's fairness claims honestly rather than implying a single gate resolves the fairness question outright. | VERIFIED verified 2026-08-25 |
| He, P., Gao, J., & Chen, W. (2021). DeBERTaV3: Improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. arXiv:2111.09543. | The architecture-family paper for the DeBERTa-v3-base encoder Impacter Pathway's scoring pipeline is built on, per the live, confirmed Model Card (impacterpathway.com/model-card). Supports the S3 model-facts framing. | VERIFIED verified 2026-08-25 |
| IMPACTER Education Corp. (2026). IMPACTER Model Card, Version 6.0: CORAL Ordinal Regression Models. | The sole authorized source for any model-version accuracy figure appearing in the Brief. | INTERNAL verified 2026-08-25 |
| IMPACTER Education Corp. (2026). A stratified, human-in-the-loop sampling layer for continuous calibration of a production rubric-aligned ordinal scoring pipeline (Technical Note, Model Suite v6.0). | Supports S3's description of Impacter Pathway's continuous human-in-the-loop calibration practice as the operational complement to the academic evaluation-standards literature, and is S7's production-side evidence that human calibration is a standing weekly practice rather than a one-time validation event. | INTERNAL verified 2026-08-25 |
Section 4 · Process & Outcome Indicators · 8 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| Scott, T. M., Gage, N. A., Hirn, R. G., Lingo, A. S., & Burt, J. (2019). An examination of the association between MTSS implementation fidelity measures and student outcomes. Preventing School Failure: Alternative Education for Children and Youth, 63(4), 308–316. doi 10.1080/1045988X.2019.1605971 | Direct empirical basis for BHPM's decision to track process/fidelity and outcome indicators as separate, independently reportable families rather than treating one as a proxy for the other. | VERIFIED verified 2026-08-25 |
| Horner, R. H., Sugai, G., & Anderson, C. M. (2010). Examining the evidence base for school-wide positive behavior support. Focus on Exceptional Children, 42(8), 1–14. | Supplies the framework-level (not single-practice) evidence-quality standard S4 uses to justify evaluating BHPM's own tiered indicator system as a whole rather than indicator-by-indicator. | VERIFIED verified 2026-08-25 |
| McIntosh, K., Massar, M. M., Algozzine, R. F., George, H. P., Horner, R. H., Lewis, T. J., & Swain-Bradway, J. (2017). Technical adequacy of the SWPBIS Tiered Fidelity Inventory. Journal of Positive Behavior Interventions, 19(1), 3–13. doi 10.1177/1098300716637193 | Field-standard, psychometrically validated instrument that BHPM's process/fidelity indicators lean on wherever a site-level PBIS fidelity score is called for. | VERIFIED verified 2026-08-25 |
| McIntosh, K., Herman, K., Bradshaw, C., & Simonsen, B. (2023). IES MTSS-B trial: Key takeaways for district and state leaders. Center on PBIS, University of Oregon. | The single independent RCT large enough to trust in this space, and the direct evidentiary basis for designing BHPM's outcome and contextual indicators to be disaggregated by subgroup and need tier from the start. | CANONICAL verified 2026-08-25 |
| Substance Abuse and Mental Health Services Administration. (2026). Project AWARE: School campus mental health. U.S. Department of Health and Human Services. | Comparable federal reporting-category precedent for BHPM's service-utilization domain (partnerships, training reach, referral volume), borrowed as reporting categories only, never as evidence of causal program effect. | CANONICAL verified 2026-08-25 |
| Cal. Welf. & Inst. Code § 5961.4 (2024). | The statutory basis for the CYBHI Fee Schedule Program, and the legal specificity behind BHPM's service-utilization indicator tying screening activity to a reimbursable service touchpoint. | CANONICAL verified 2026-08-25 |
| California Department of Health Care Services. (2026). Children and Youth Behavioral Health Initiative (CYBHI) Fee Schedule Program. | The in-state, statewide multi-payer reporting infrastructure BHPM's own service-utilization indicators can eventually connect to as a district's CYBHI cohort status and billing infrastructure come online. | CANONICAL verified 2026-08-25 |
| American Medical Association. (2026). CPT code 96127: Brief emotional/behavioral assessment, with scoring and documentation, per standardized instrument. Current Procedural Terminology. | The specific billing-code detail that ties a BHPM screening event to a documented, reimbursable service-utilization touchpoint wherever a site's billing infrastructure already supports it. | CANONICAL verified 2026-08-25 |
Section 5 · Implementation Science · 6 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| Fixsen, D. L., Naoom, S. F., Blase, K. A., Friedman, R. M., & Wallace, F. (2005). Implementation research: A synthesis of the literature (FMHI Publication #231). National Implementation Research Network, University of South Florida, Louis de la Parte Florida Mental Health Institute. | Supplies the stage/component framework the Brief uses to argue that BHPM's readiness-to-deployment-to-TTA sequence (D1 to D2 to D3) is a deliberate, evidence-grounded implementation pathway rather than an ad hoc rollout. | CANONICAL verified 2026-08-25 |
| Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65-76. doi 10.1007/s10488-010-0319-7 | Gives the Metrics Brief a validated eight-domain taxonomy for organizing process/fidelity indicators separately from outcome indicators within the composite index and evidence tables. | VERIFIED verified 2026-08-25 |
| Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America's schools can get better at getting better. Harvard Education Press. | Provides the direct literature basis for BHPM's D4/D5 Collaborative Learning Group design, framing the quarterly learning-group cycle as PDSA-style networked improvement rather than a one-off convening. | CANONICAL verified 2026-08-25 |
| Baffsky, R., Ivers, R., Cullen, P., Wang, J., McGillivray, L., & Torok, M. (2023). Strategies for enhancing the implementation of universal mental health prevention programs in schools: A systematic review. Prevention Science, 24(2), 337-352. doi 10.1007/s11121-022-01434-9 | Directly supports the case that BHPM's continuous platform-analytics approach to monitoring and feedback, rather than annual recall surveys, sits on the strongest evidence stream this review identifies for school-based behavioral-health implementation. | VERIFIED verified 2026-08-25 |
| Deming, W. E. (1986). Out of the crisis. Massachusetts Institute of Technology, Center for Advanced Engineering Study. | Establishes the improvement-science lineage that BHPM's continuous, cycle-based process-evaluation design draws on, rather than treating PDSA as if it originated with the education-sector literature alone. | CANONICAL verified 2026-08-25 |
| Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, Article 50. doi 10.1186/1748-5908-4-50 | Gives BHPM's Deliverable 1 site readiness assessment a validated, construct-level checklist for the organizational-context factors CFIR's inner-setting domain identifies as separable from technical capacity, complementing rather than replacing the Fixsen driver framework already anchoring this section. | VERIFIED verified 2026-08-25 |
Section 6 · Composite Index Construction · 5 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| OECD, & European Commission Joint Research Center. (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. doi 10.1787/9789264043466-en | The direct, step-by-step methodological framework the Behavioral Health Performance Index's entire construction (sub-indices, normalization, weighting, aggregation, banding, versioning) is built on. | CANONICAL verified 2026-08-25 |
| McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates. doi 10.4324/9781410601087 | The psychometric basis for BHPI's commitment to report composite and sub-index reliability using omega rather than Cronbach's alpha, given that each domain sub-index mixes heterogeneous indicator types. | VERIFIED verified 2026-08-25 |
| Seastrom, M. (2010). Statistical methods for protecting personally identifiable information in aggregate reporting (NCES 2011-603; SLDS Technical Brief 3). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. | The federal basis for BHPI's default minimum reporting-group size of ten students plus complementary suppression, set with SCCOE data governance. | VERIFIED verified 2026-08-25 |
| California Department of Education. (2025). California School Dashboard technical guide: 2025 Dashboard. | The in-state design precedent BHPI's own performance-banding approach adapts, so LEA teams read behavioral health performance in the same visual grammar they already use for academic and climate indicators. | CANONICAL verified 2026-08-25 |
| Saisana, M., Saltelli, A., & Tarantola, S. (2005). Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society: Series A (Statistics in Society), 168(2), 307–323. doi 10.1111/j.1467-985X.2005.00350.x | Supplies the specific statistical technique (variance-based/Sobol-type decomposition) behind the general 'mandatory sensitivity analysis' requirement the OECD/JRC Handbook states, and grounds the Index Methodology page's plan to report how much of BHPI's score and rank uncertainty is attributable to the weight set specifically versus the normalization or aggregation choice, rather than reporting one undifferentiated robustness number. | VERIFIED verified 2026-08-25 |
Section 7 · Research Lineage · 12 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| Cao, W., Mirjalili, V., & Raschka, S. (2020). Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters, 140, 325–331. doi 10.1016/j.patrec.2020.11.008 | Direct methodological citation for the CORAL ordinal-regression head named in Impacter Pathway's own architecture (DeBERTa-v3-base + CORAL, per the live Model Card). Grounds the 'discriminative ordinal pipeline, not generative sampling' argument made in S2/S3, and the same head named in S7's production-architecture discussion. | VERIFIED verified 2026-08-25 |
| IMPACTER Education Corp. (2026). IMPACTER Model Card, Version 6.0: CORAL Ordinal Regression Models. | The sole authorized source for any model-version accuracy figure appearing in the Brief. | INTERNAL verified 2026-08-25 |
| IMPACTER Education Corp. (2026). A stratified, human-in-the-loop sampling layer for continuous calibration of a production rubric-aligned ordinal scoring pipeline (Technical Note, Model Suite v6.0). | Supports S3's description of Impacter Pathway's continuous human-in-the-loop calibration practice as the operational complement to the academic evaluation-standards literature, and is S7's production-side evidence that human calibration is a standing weekly practice rather than a one-time validation event. | INTERNAL verified 2026-08-25 |
| Ober, T. M., Zhang, S., Zapata-Rivera, D., Schroeder, N. L., & Botelho, A. F. (2026). Using LLMs to identify indicators of persistence from students' dialogues with a pedagogical agent. Journal of Educational Data Mining, 18(1), 208-243. doi 10.5281/zenodo.18852441 | Directly supports the argument that rubric-first, operationally precise construct definition is what a generative LLM-coding pipeline needs to be reliable, and by extension, why a discriminative, deterministic, frozen-and-versioned ordinal scoring architecture avoids the reproducibility problem this paper documents. | VERIFIED verified 2026-08-25 |
| Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023). | Documents the class of instability and reproducibility risk inherent in generative, sampling-based LLM reasoning, the same risk category Ober et al. (2026) found in LLM-based construct coding, and the basis for distinguishing Impacter Pathway's deterministic discriminative pipeline from generative LLM coding. | VERIFIED verified 2026-08-25 |
| Arnold, A., Liu, Y., & Abe, N. (2007). Temporal causal modeling with graphical Granger methods. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 66–75). Association for Computing Machinery. doi 10.1145/1281192.1281203 | Documents the specific causal-inference toolkit behind Impacter Pathway's Senior Scientific Advisor and scoring-pipeline architect, framed as the planned analytic foundation for BHPM's Year 2 upstream-lever and dosage analysis with Stanford Medicine, not as evidence about the screener itself. | VERIFIED verified 2026-08-25 |
| Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438. doi 10.2307/1912791 | The direct intellectual ancestor of the graphical Granger-causality methods in Arnold, Liu & Abe (2007): documents that Impacter Pathway's Senior Scientific Advisor's causal-inference toolkit traces to a specific, checkable methodological origin rather than being a generic 'machine learning' credential. | VERIFIED verified 2026-08-25 |
| IMPACTER Education Corp. (2026). From self-report to student voice: A guide to performance-based behavioral health screening (Version 2.1). | The plainer-language, partner-facing statement of the same design choice S7 documents academically: rubric-aligned scoring against authentic student language rather than a fixed self-report checklist. | INTERNAL verified 2026-08-25 |
| IMPACTER Education Corp. (2026). A human-friendly guide to IMPACTER's machine learning model: A resource for our partners. | The organization's own plain-language statement of the human-leads/machine-learning-scales principle that S7 closes its research-lineage argument with. | INTERNAL verified 2026-08-25 |
| Hirshberg, M. J., Colaianne, B. A., Greenberg, M. T., Inkelas, K. K., Davidson, R. J., Germano, D., Dunne, J. D., & Roeser, R. W. (2022). Can the academic and experiential study of flourishing improve flourishing in college students? A multi-university study. Mindfulness, 13(9), 2243–2256. doi 10.1007/s12671-022-01952-1 | Design rationale, not validity evidence, for why Impacter Pathway's prompts ask students to reflect in their own words rather than select from a fixed checklist. | VERIFIED verified 2026-08-25 |
| Pennebaker, J. W., & Beall, S. K. (1986). Confronting a traumatic event: Toward an understanding of inhibition and disease. Journal of Abnormal Psychology, 95(3), 274–281. doi 10.1037/0021-843X.95.3.274 | The earlier link in the same reflective-practice research lineage Hirshberg et al. (2022) extends: evidence that structured reflection on one's own experience is itself a mechanism with measurable effects, not merely a delivery format: design rationale for Impacter Pathway's open-ended prompts, not validity evidence for the screener. | VERIFIED verified 2026-08-25 |
| Saab, B. J., Georgiou, J., Nath, A., Lee, F. J. S., Wang, M., Michalon, A., Liu, F., Mansuy, I. M., & Roder, J. C. (2009). NCS-1 in the dentate gyrus promotes exploration, synaptic plasticity, and rapid acquisition of spatial memory. Neuron, 63(5), 643–656. doi 10.1016/j.neuron.2009.08.014 | Cited strictly as Impacter Pathway's stated founding neurobiological inspiration for taking the biological basis of exploratory behavior seriously as a construct worth measuring, explicitly NOT validity evidence for the screener and not evidence about human behavioral health outcomes; this boundary must be preserved in any downstream reuse. | VERIFIED verified 2026-08-25 |
Cited Outside The Seven Sections · 2 Sources
| Source | What It Establishes | Provenance |
|---|---|---|
| Elo, A. E. (1978). The rating of chessplayers, past and present. Arco Publishing. | Flagged in the Index Methodology page as a literature worth considering for the BHPI change-axis calculation specifically (weighting year-over-year movement by measurement confidence rather than by simple subtraction), not as part of the core four-domain composite construction itself. | CANONICAL verified 2026-08-25 |
| Glickman, M. E. (1999). Parameter estimation in large dynamic paired comparison experiments. Journal of the Royal Statistical Society: Series C (Applied Statistics), 48(3), 377–394. doi 10.1111/1467-9876.00159 | Cited alongside Elo (1978) in the Index Methodology page as the more rigorous, uncertainty-aware paired-comparison alternative worth considering for BHPI's year-over-year change-axis calculation. | VERIFIED verified 2026-08-25 |
How to read provenance
VERIFIED means the entry was resolved to a live DOI, publisher record or open-access record on the date shown. INTERNAL means an Impacter Pathway work product. CANONICAL means the approved Model Card: the single source for any model-performance figure quoted anywhere in this record.
Why This Matters
Every claim in the seven sections resolves to an entry here, and every entry says how and when it was checked.