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Record W2396130171 · doi:10.1177/0008417416637186

Occupational therapy in primary care: Results from a national survey

2016· article· en· W2396130171 on OpenAlexvenueaboutno aff
Catherine Donnelly, Leanne Leclair, Pamela Wener, Carri Hand, Lori Letts

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapySnowball samplingMedicineIntervention (counseling)Descriptive statisticsFamily medicineNursingPromotion (chess)Primary careNonprobability samplingEnvironmental healthPsychiatryPopulation

Abstract

fetched live from OpenAlex

BACKGROUND.: To support integration of occupational therapy in primary care and research in this area, it is critical to document examples of occupational therapy in primary care. PURPOSE.: This study describes occupational therapy roles and models of practice used in primary care. METHOD.: An electronic survey was sent to occupational therapists across Canada. Participants were identified using purposive and snowball sampling strategies. Descriptive statistics were used to analyze the data. FINDINGS.: Respondents ( n = 52) were almost exclusively working on interprofessional teams. Intervention was provided most frequently to individual clients, and services were provided both within the home/community and in the clinic. Occupational therapists offered a range of health promotion and prevention services, predominantly to adults and older adults. A number of supports and barriers to the integration of occupational therapy were identified. IMPLICATIONS.: A growing number of occupational therapists are working in primary care providing a broad range of services across the life span.

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)0.007
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 score0.365
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.403
GPT teacher head0.506
Teacher spread0.103 · 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

Citations37
Published2016
Admission routes2
Has abstractyes

Explore more

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