MétaCan
Menu
Back to cohort
Record W2104841065 · doi:10.1111/medu.12522

Exploring stakeholders’ views of medical education research priorities: a national survey

2014· article· en· W2104841065 on OpenAlexaboutno aff
Ashley Dennis, Jennifer Cleland, Peter Johnston, Jean Ker, Murray Lough, Charlotte E. Rees

Bibliographic record

VenueMedical Education · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersAustralian and New Zealand Association for Health Professional EducatorsNHS Education for Scotland
KeywordsWorkforceCurriculumContext (archaeology)Medical educationFocus groupQualitative propertyPsychologyKnowledge managementPublic relationsMedicinePedagogyPolitical scienceBusinessComputer scienceMarketing

Abstract

fetched live from OpenAlex

CONTEXT: Setting research priorities is important when exploring complex issues with limited resources. Only two countries (Canada and New Zealand) have previously conducted priority-setting exercises for medical education research (MER). This study aimed to identify the views of multiple stakeholders on MER priorities in Scotland. METHODS: This study utilised a two-stage design to explore the views of stakeholders across the medical education continuum using online questionnaires. In Stage 1, key informants outlined their top three MER priorities and justified their choices. In Stage 2, participants rated 21 topics generated in Stage 1 according to importance and identified or justified their top priorities. A combination of qualitative (i.e. framework analysis) and quantitative (e.g. exploratory factor analysis) data analyses were employed. RESULTS: Views were gathered from over 1300 stakeholders. A total of 21 subthemes (or priority areas) identified in Stage 1 were explored further in Stage 2. The 21 items loaded onto five factors: the culture of learning together in the workplace; enhancing and valuing the role of educators; curriculum integration and innovation; bridging the gap between assessment and feedback, and building a resilient workforce. Within Stage 2, the top priority subthemes were: balancing conflicts between service and training; providing useful feedback; promoting resiliency and well-being; creating an effective workplace learning culture; selecting and recruiting doctors to reflect need, and ensuring that curricula prepare trainees for practice. Participant characteristics were related to the perceived importance of the factors. Finally, five themes explaining why participants prioritised items were identified: patient safety; quality of care; investing for the future; policy and political agendas, and evidence-based education. CONCLUSIONS: This study indicates that, across the spectrum of stakeholders and geography, certain MER priorities are consistently identified. These priority areas are in harmony with a range of current drivers in UK medical education. They provide a platform of evidence on which to base decisions about MER programmes in Scotland and beyond.

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)0.020
metaresearch head score (Gemma)0.130
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.130
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.490
GPT teacher head0.508
Teacher spread0.017 · 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 designNot applicable
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

Citations45
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueMedical EducationSame topicInnovations in Medical EducationFrench-language works237,207