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Record W2154503895 · doi:10.1080/13561820802697628

The Practice-based Small Group Learning Programme: Experiences of learners in multi-professional groups

2009· article· en· W2154503895 on OpenAlexaboutno aff
Githa Kanisin-Overton, Peter McCalister, Diane Kelly, Ronald MacVicar

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningSmall group learningContinuing professional developmentGroup cohesivenessCohesion (chemistry)PsychologyProfessional developmentMedical educationPedagogyMedicineSocial psychology

Abstract

fetched live from OpenAlex

This study describes the experiences of General Practitioners (GPs) and Practice Nurses (PNs) as they came together and engaged in the Practice-based Small Group Learning (PBSGL) programme. Based on principles of adult and small group learning, PBSGL, which was developed in Canada, was used for the first time for the Continuing Professional Development (CPD) of multi-professional groups in the UK. The findings detail the main reasons GPs and PNs participated in PBSGL, the nature of interaction and development of cohesion in the groups, factors influencing contribution to discussions, the learning process, and outcomes for learners. Respect shown for different roles and perspectives enabled participants to be open about gaps in their knowledge and to ask questions. A mutual keenness to understand the perspectives of and learn from the other profession emerges as a key ingredient for learners to feel that their learning needs were met. The learning process in the groups came close to transformative learning--there were changes in perspectives, acquisition of new knowledge and increased self-esteem. The appropriateness of the PBSGL approach for the CPD of mixed groups of GPs and PNs is discussed.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 science0.0010.000
Research integrity0.0000.003
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 head0.036
GPT teacher head0.442
Teacher spread0.406 · 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

Citations19
Published2009
Admission routes1
Has abstractyes

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