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Record W2020605244 · doi:10.1177/0008417415576776

Communities of practice: Exploring enablers and barriers with school health clinicians

2015· article· en· W2020605244 on OpenAlexfundvenueno aff
Gwenyth I. Roberts

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersAlberta Health Services
KeywordsSituatedPaceCreativityPsychologyInterpretative phenomenological analysisMedical educationCommunity of practiceSituated learningOccupational therapyDescriptive statisticsHealth carePedagogyNursingQualitative researchSociologyMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: It is imperative that therapists keep pace with relevant knowledge and reflect on their practice. Community of practice (CoP) sessions provide clinicians with a forum to share stories, exchange information, and foster scholarly practice. Studies on CoPs in health care are limited. PURPOSE: The purpose of this study was to explore the enablers and barriers to participation in CoPs. METHOD: Occupational therapists and physiotherapists in a school health program participated in a questionnaire (n=18) and semi-structured interviews (n=14). Analyses were completed using descriptive statistics (questionnaires) and interpretative phenomenological analysis (interviews). FINDINGS: Six themes describing participation in CoPs emerged: structure-engagement, learning, growth-becoming, fellowship-belonging, implementation-doing, and contributing-influencing. The findings highlight the importance of situated learning, reflection, and creativity to influence practice through discussions of ideas, research, and resources in small supportive groups of like-minded individuals with an informal, self-directed structure. IMPLICATIONS: Features to consider when implementing CoPs in the workplace are 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.585
GPT teacher head0.547
Teacher spread0.038 · 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.

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

Citations21
Published2015
Admission routes2
Has abstractyes

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