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Record W4387878409 · doi:10.1002/lrh2.10399

Enrichment of core competencies to maximize health system impact: An analysis of an embedded research training program

2023· article· en· W4387878409 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLearning Health Systems · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoSt. Michael's HospitalInstitute of Health Services and Policy Research
FundersMemorial University of NewfoundlandUniversity of Toronto
KeywordsMentorshipMedical educationDyadCore competencyPsychologySupervisorExperiential learningCareer developmentMedicinePedagogyManagement

Abstract

fetched live from OpenAlex

Abstract Introduction The Health System Impact (HSI) Fellowship is an embedded research training program that aims to prepare doctoral trainees and postdoctoral fellows for stronger career readiness and greater impact as emerging leaders within and beyond the academy, including in learning health systems (LHS). The program supports fellows to develop 10 leadership and research competencies that comprise the Enriched Core Competency Framework in Health Services and Policy Research through a combination of experiential learning, mentorship, and professional development training. This study tracks competency development of HSI fellows over time and examines fellows' perspectives on which program design elements support their competency development. Methods A competency assessment tool developed for the program was independently completed by 95 postdoctoral and 36 doctoral fellows (self‐assessments) and their respective 203 dyad (academic and health system) supervisors in the 2017 to 2019 program cohorts, who independently rated the strength of fellows' 10 competencies at baseline and several points thereafter. Competency strength ratings were analyzed to understand change over time and differences in ratings across groups (between fellows' sex, supervisor type, and supervisor vs. fellow). Program design element ratings were examined to understand perspectives on their contribution toward fellows' competency development. Results Fellows' competency strength significantly improved in all 10 domains over time, based on independent assessments by the fellows and their dyad supervisors. Supervisors tended to rate the fellows' competency strength higher than the fellows did. Differences in competency ratings between male and female fellows (self‐assessments) and between academic and health system supervisors were either negligble or not significant. Fellows identified all nine program design elements as enriching their competency development. Conclusion The HSI Fellowship provides an opportunity for fellows to develop the full suite of enriched core competencies and to prepare a cadre of emerging leaders with the skills and experience to contribute to the advancement of LHS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.210
GPT teacher head0.532
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it