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Record W2652445161 · doi:10.1177/0008417417709483

Supporting occupational therapists implementing a capacity-building model in schools

2017· article· en· W2652445161 on OpenAlexfundvenueno aff
Nancy A. Pollock, Leah Dix, Sandra Sahagian Whalen, Wenonah Campbell, Cheryl Missiuna

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersOntario Ministry of Health and Long-Term Care
KeywordsOccupational therapyCapacity buildingPsychologyMedical educationMedicinePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Adopting a new model of clinical practice is complex. Professional development programs based on best-practice principles may facilitate this process. PURPOSE: This paper describes the development and evaluation of a multifaceted professional development program designed to support school-based occupational therapists to deliver a capacity-building model of service. METHOD: Twenty-two therapists participated in the program; completed pre-post evaluations of knowledge, skills, and beliefs; evaluated specific components of the training program; and participated in focus groups. Quantitative data were analyzed using descriptive and inferential statistics. Qualitative data were analyzed using a directed content analysis. FINDINGS: Therapists' perceptions of their knowledge and skills showed statistically significant change. Both training and mentorship were highly valued; however, having opportunities to build peer networks was considered essential. IMPLICATIONS: Multifaceted professional development programs designed using best-practice principles are an important mechanism for facilitating practice change. Including a process for peer support is advised.

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)0.015
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0030.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.450
GPT teacher head0.575
Teacher spread0.124 · 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 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

Citations28
Published2017
Admission routes2
Has abstractyes

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