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Record W2044682549 · doi:10.1007/s40037-015-0158-z

Supervised near-peer clinical teaching in the ambulatory clinic: an exploratory study of family medicine residents’ perspectives

2015· article· en· W2044682549 on OpenAlexaffabout
Daniel James Ince-Cushman, Teresa Rudkin, Ellen Rosenberg

Bibliographic record

VenuePerspectives on Medical Education · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsPreceptorMedicineContext (archaeology)Medical educationNursingExploratory researchFamily medicinePeer learningPsychologyPedagogy

Abstract

fetched live from OpenAlex

Near-peer teaching is used extensively in hospital-based rotations but its use in ambulatory care is less well studied. The objective of this study was to verify the benefits of near-peer teaching found in other contexts and to explore the benefits and challenges of near-peer clinical supervision unique to primary care. A qualitative descriptive design using semi-structured interviews was chosen to accomplish this. A faculty preceptor supervised senior family medicine residents as they supervised a junior resident. We then elicited residents' perceptions of the experience. The study took place at a family medicine teaching unit in Canada. Six first-year and three second-year family medicine residents participated. Both junior and senior residents agreed that near-peer clinical supervision should be an option during family medicine residency training. The senior resident was perceived to benefit the most. Near-peer teaching was found to promote self-reflection and confidence in the supervising resident. Residents felt that observation by a faculty preceptor was required. In conclusion, the benefits of near-peer teaching previously described in hospital settings can be extended to ambulatory care training programmes. However, the perceived need for direct observation in a primary care context may make it more challenging to implement.

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)0.016
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.128
GPT teacher head0.485
Teacher spread0.357 · 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

Citations24
Published2015
Admission routes2
Has abstractyes

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