MétaCan
Menu
← Back to cohort
Record W7020126731

The learning experiences of general practice registrars in the South East of Scotland

2005· other· en· W7020126731 on OpenAlexaboutno aff

Bibliographic record

VenueOpenGrey (Institut de l'Information Scientifique et Technique) · 2005
Typeother
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral Packet Radio ServiceCohortFocus groupQuarter (Canadian coin)TrainerGeneral practice
DOInot available

Abstract

fetched live from OpenAlex

To train to be a general practitioner in the U.K. a doctor must spend two years in hospital training posts and one year in general practice as a general practice registrar (GPR). Concern has been expressed in the literature about both the duration and adequacy of general practice training. A literature review identified that there was limited knowledge of and understanding about the learning experiences of GPRs. The aim of the study was to describe and interpret the learning experiences of GPRs in the South East of Scotland during their year in general practice. The methodology was derived from Denzin's concept of Interpretivism and involved in depth interviews over time with GPRs and thick description to capture and interpret the GPRs learning experiences. Two cohorts of 24 GPRs were recruited, cohort one ran from September 2002 to July 2003 and cohort two from September 2003 to August 2004. The GPRs were interviewed on three occasions during their year. In addition to the interviews six GPR focus groups and six GP trainer focus groups were held over the period December 2002 to September 2003. 21 GPRs in cohort one completed all three interviews and 20 GPRs in cohort two. All the participating GPRs completed at least two interviews. The results were interpreted within the educational concept of the curriculum. Four main curricula were identified during the GPR year: these were the formal, assessment, individual and hidden. Each independently contributed to the GPRs learning and also interacted synergistically at various times during the year. In the last quarter of the year there was a tension between the requirements of the assessment and individual curricula. The individual curriculum which was composed of the GPRs clinical experiences and in particular epiphanies was the main driver of GPR learning. Epiphanies were identified by GPRs as having the most significant impact on their learning. Central to this learning was the contribution of their general practice trainer who supported their learning both through the development of the practice learning environment and the promotion of reflection and self directed learning. GPR learning during the year was an iterative process, which involved a reflective and supported interaction between the GPR, their clinical experiences, epiphanies and their trainer. Through this process the GPRs became self directed and reflective learners and developed individual learning networks which led to changes in the way they practiced medicine. This process also led to the socialisation of their learning and promoted their integration into the culture of working general practice, through which they were exposed to the working realities of life as a general practitioner and these experiences had a critical effect on their future career choice. A number of important policy implications were identified which have implications for the present and future direction of training for general practice. The process of thick description and the longitudinal nature of the study allowed for a new interpretation of the learning experiences of GPRs and added to the knowledge and understanding of how GPRs learn during their training.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0040.003
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.330
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueOpenGrey (Institut de l'Information Scientifique et Technique)→Same topicInnovations in Medical Education→French-language works237,207→