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Record W4411042393 · doi:10.3389/frhs.2025.1589643

Considerations for engaging in patient-oriented research with injured workers

2025· article· en· W4411042393 on OpenAlexaff
Gagan Nagra, Pam Hung, M. Robert Peters, Christine Guptill, Victor E. Ezeugwu, Lynn Cooper, Beverley McKeen, Douglas P. Gross

Bibliographic record

VenueFrontiers in Health Services · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsCanada Auto WorkersUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsCompensation (psychology)Vulnerability (computing)Bridge (graph theory)Process (computing)Health careWork (physics)Knowledge translationKnowledge transferOccupational therapyKey (lock)MedicineKnowledge managementPsychologyMedical educationNursingBusinessComputer scienceEngineeringPolitical scienceSocial psychologyPhysical therapy

Abstract

fetched live from OpenAlex

Background: Patient-oriented research (POR) incorporates patient-identified priorities and lived experiences into research. Despite their central role in return-to-work (RTW) planning, perspectives and priorities of injured workers are under-represented in Occupational Therapy research. Occupational therapists (OTs) play a key role in RTW research and practice, implementing evidence-based plans and patient-centered care, which positions them well to conduct POR. Purpose: The purpose of this paper is to identify considerations for POR approaches for OTs to engage injured workers in RTW research. Key issues: The engagement of injured workers as research partners is not well described or understood in POR. This paper outlines practical considerations for conducting POR with injured workers, addressing challenges such as power imbalances, communication barriers, fears of unemployment, and varying levels of vulnerability. OTs can facilitate knowledge transfer and act as knowledge brokers within the RTW process, leveraging their client-centered practice to lead research that optimally engages injured workers. Conclusion: Conducting POR with injured workers can shed light on their interactions with health, insurance, and compensation systems. POR approaches can highlight strengths and limitations of available services and systems and promote improved collaboration and knowledge translation and exchange. OTs can apply POR in research and practice to bridge this gap.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.512
Teacher spread0.401 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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