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Record W2126560047 · doi:10.3109/0142159x.2013.827330

Students’ experience of prison health education during medical school

2013· article· en· W2126560047 on OpenAlexaff
Heather Filek, James M. Harris, John J. Koehn, John L. Oliffe, Jane A. Buxton, Ruth Elwood Martin

Bibliographic record

VenueMedical Teacher · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPrisonMedical educationThematic analysisAccountabilityCurriculumTeamworkMedical schoolPsychologyMedicineNursingQualitative researchPedagogySociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Social responsibility and accountability can be important core values in medical education. At the University of British Columbia, undergraduate medical students engage in prison health community service-learning opportunities in regional correctional facilities. METHODS: To describe the impact of prison health exposure on pre-clinical medical students, in-depth individual interviews were conducted with individuals who had participated in a prison health medical education program. All interviews were transcribed verbatim, and interpretive descriptive methods were used to inductively derive thematic findings to describe students' experiences. RESULTS: Major themes emerged as students reported how (1) exposure to incarcerated populations increases students' insight into issues that diverse marginalized sub-populations encounter; (2) positive interactions with the incarcerated individuals enhances relationship building; (3) collaboration reinforces teamwork skills and (4) community placements garner important learning opportunities within the medical school curriculum. CONCLUSIONS: Our findings demonstrated that pre-clinical exposure to incarcerated individuals and prison health education provided a unique setting for medical students to develop an increased sense of social responsibility and accountability.

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)0.001
metaresearch head score (Gemma)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0480.000

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 head0.022
GPT teacher head0.401
Teacher spread0.379 · 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 designQualitative
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

Citations30
Published2013
Admission routes1
Has abstractyes

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