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Record W2010117781 · doi:10.1177/000841740206900304

The Impact of Reimbursement Systems on Occupational Therapy Practice in Canada and the United States of America

2002· review· en· W2010117781 on OpenAlexvenueaboutno aff
Lyn Jongbloed, Toby Wendland

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2002
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsReimbursementOccupational therapyPsychological interventionAutonomyGovernment (linguistics)Health careMedicineScope of practiceBusinessService delivery frameworkNursingService (business)Economic growthPolitical scienceMarketingPhysical therapyEconomics

Abstract

fetched live from OpenAlex

Different funding and cost-control mechanisms in Canada and the United States of America (USA) have a powerful influence on occupational therapy practice in each country. Canada's public health insurance system emphasizes access to health care services based on medical need. Costs are controlled at the provincial government level by limiting the capacity of facilities and personnel. Occupational therapists in publicly-funded settings have considerable professional autonomy to use occupational therapy theoretical models and to be client-centred. The measurement of outcomes is not always required and the interventions of individual occupational therapists are infrequently scrutinized. The USA has no universal, publicly-funded, comprehensive health insurance. Health care policies are driven by financial priorities and cost control occurs at the service delivery level. Insurance companies define the scope of occupational therapy practice by identifying what services they will pay for and they scrutinize occupational therapy interventions. The emphasis on effectiveness and efficiency leads to critical examination of interventions by therapists. Canadian occupational therapists can learn much from their colleagues in the USA in this area.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.917
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.349
GPT teacher head0.543
Teacher spread0.195 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2002
Admission routes2
Has abstractyes

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