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Record W2737968297 · doi:10.18552/ijpblhsc.v5i1.385

Comparing Occupational Therapy Students' Competency Scores: 2:1 versus 1:1 Fieldwork

2017· article· en· W2737968297 on OpenAlexafffund
Marisa Short, Candace Letham, Leanne M. Currie, Donna Drynan

Bibliographic record

VenueInternational Journal of Practice-based Learning in Health and Social Care · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of British ColumbiaFraser Health
FundersUniversity of British Columbia
KeywordsOccupational therapyMedical educationMedicinePsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Fieldwork is essential to occupational therapy students’ development of professional competencies. Fieldwork models using student-to-fieldwork educator ratios of 1:1 and 2:1 are widely used, however, quantitative research exploring differences in student competencies between these two models is scarce. The objective of this study was to determine if development of student competencies differ between 2:1 and 1:1 fieldwork models during an occupational therapy educational program. A retrospective study using two years of occupational therapy students’ competency ratings by fieldwork supervisors (N = 95 students; N = 355 fieldwork events) was performed. The Competency Based Fieldwork Evaluation for Occupational Therapists tool was used to assess student competencies at each fieldwork rotation (n = 5 placements). Independent samples t-tests were used to compare students’ scores during 1:1 and 2:1 placements. No significant differences were noted in student competencies following participation in 2:1 and 1:1 placements. It was concluded that students are equally well prepared for practice if they have 1:1 or 2:1 fieldwork experiences.

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)0.003
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.219
GPT teacher head0.575
Teacher spread0.355 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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Same venueInternational Journal of Practice-based Learning in Health and Social CareSame topicOccupational Therapy Practice and ResearchFrench-language works237,207