MétaCan
Menu
Back to cohort
Record W1986818007 · doi:10.3109/0142159x.2012.733450

Educator perceptions of the relationship between education innovations and improved health

2012· article· en· W1986818007 on OpenAlexaff
Stacey Friedman, Lawrence C. Loh, William P. Burdick

Bibliographic record

VenueMedical Teacher · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of Toronto
FundersU.S. President’s Emergency Plan for AIDS Relief
KeywordsPerceptionHealth professionsMedical educationPsychologyMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Education innovations by health professions faculty are shaped by faculty conceptualizations of the pathway between their innovations and changes in health of communities. AIMS: We aimed to explore how existing theories about the relationship between education and health are attended to, interpreted, and applied by faculty in different national contexts. METHODS: We compared existing theoretical frameworks to perceptions of "front line" faculty. Fellows in Brazil- and India-based FAIMER faculty development programs were asked via questionnaires about the contribution of their education innovation projects to health improvements. RESULTS: Faculty identified pathways to improved societal health via increased quality, and to a lesser extent relevance, of education. Relationships between increased quantity of education and improved health were focused on faculty development. Faculty from both countries noted the value for health outcomes of innovations that affect networks and partnerships with other institutions. Faculty from India identified pathways to improved societal health via changes to instructional more than institutional processes. CONCLUSIONS: Results indicate where there are gaps in existing theories, a need to raise awareness about potential pathways to improving health via education changes, and opportunities for more detailed understanding of mechanisms of change via in-depth research.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.132
GPT teacher head0.517
Teacher spread0.385 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueMedical TeacherSame topicPublic Health Policies and EducationFrench-language works237,207