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Record W2574870319 · doi:10.1177/1539449216687528

International Occupational Therapy Research Priorities

2017· article· en· W2574870319 on OpenAlexaff
Lynette Mackenzie, Susan Coppola, Liliana Alvarez, Lolita Cibule, S. V. Maltsev, Siew Yim Loh, Tecla Mlambo, Moses N. Ikiugu, Zdenka Pihlar, Sarinya Sriphetcharawut, Sue Baptiste, Richard Ledgerd

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsWestern University
Fundersnot available
KeywordsOccupational therapyDelphi methodAccreditationAttritionDelphiPolitical scienceMedical educationPublic relationsMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Occupational therapy is a global profession represented by the World Federation of Occupational Therapists (WFOT). International research priorities are needed for strategic guidance on global occupational therapy practice. The objective of this study was to develop international research priorities to reflect global occupational therapy practice. A Delphi study using three rounds of electronic surveys, distributed to WFOT member organizations and WFOT accredited universities, was conducted. Data were analyzed after each round, and priorities were presented for rating and ranking in order of importance. Forty-six (53%) out of 87 WFOT member countries participated in the Delphi process. Eight research priorities were confirmed by the final electronic survey round. Differences were observed in rankings given by member organizations and university respondents. Despite attrition at Round 3, the final research priorities will help to focus research efforts in occupational therapy globally. Follow-up research is needed to determine how the research priorities are being adopted internationally.

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)0.133
metaresearch head score (Gemma)0.095
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: none
Teacher disagreement score0.133
Threshold uncertainty score0.705

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1330.095
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.015
Science and technology studies0.0070.004
Scholarly communication0.0160.011
Open science0.0030.012
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0330.006

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 head0.696
GPT teacher head0.665
Teacher spread0.032 · 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

Citations57
Published2017
Admission routes1
Has abstractyes

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