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Record W1981393956 · doi:10.2182/cjot.2010.77.3.2

1000 Fieldwork Hours: Analysis of Multi-Site Evidence

2010· article· en· W1981393956 on OpenAlexaffvenue
Jeffrey D. Holmes, Ann Bossers, Helene J. Polatajko, Donna Drynan, MaryBeth Gallagher, Clare O’Sullivan, Anita Slade, Jill Stier, Caroline Storr, Julie L. Denney

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsCompetence (human resources)Entry LevelOccupational therapyMedical educationScale (ratio)MedicinePsychologyNursingPhysical therapyGeographySocial psychologyCartography

Abstract

fetched live from OpenAlex

BACKGROUND: Internationally, the World Federation of Occupational Therapists has established a minimum of 1,000 hours as the fieldwork standard. PURPOSE: To examine student development in fieldwork across placements to determine if students achieve entry-level competence after completion of 1,000 hours of fieldwork. METHODS: Archival data (N=400) from six occupational therapy programs were analyzed to examine the acquisition of fieldwork competency over time as measured by the Competency Based Fieldwork Evaluation Scale. FINDINGS: Competency scores increased with each fieldwork placement, the majority of students achieved entry-level scores upon completion of their final fieldwork placement. While, on average, some competency scores exceeded entry level by 1,000 hours, Practice Knowledge, Clinical Reasoning, and Facilitating Change fell just short. IMPLICATIONS: The identification of a plan for addressing the lower ratings in these three competencies should be considered.

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)0.035
metaresearch head score (Gemma)0.115
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.115
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0100.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.402
GPT teacher head0.552
Teacher spread0.151 · 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 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

Citations45
Published2010
Admission routes2
Has abstractyes

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