MétaCan
Menu
Back to cohort
Record W6964219899 · doi:10.25384/sage.c.4265195

Tensions Living Out Professional Values for Physical Therapists Treating Injured Workers

2018· other· en· W6964219899 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCompensation (psychology)Health careHealth professionalsService (business)Quality (philosophy)Physical healthQualitative research

Abstract

fetched live from OpenAlex

Health care services provided by workers’ compensation systems aim to facilitate recovery for injured workers. However, some features of these systems pose barriers to high quality care and challenge health care professionals in their everyday work. We used interpretive description methodology to explore ethical tensions experienced by physical therapists caring for patients with musculoskeletal injuries compensated by Workers’ Compensation Boards. We conducted in-depth interviews with 40 physical therapists and leaders in the physical therapy and workers’ compensation fields from three Canadian provinces and analyzed transcripts using concurrent and constant comparative techniques. Through our analysis, we developed inductive themes reflecting significant challenges experienced by participants in upholding three core professional values: equity, competence, and autonomy. These challenges illustrate multiple facets of physical therapists’ struggles to uphold moral commitments and preserve their sense of professional integrity while providing care to injured workers within a complex health service system.

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)0.053
metaresearch head score (Gemma)0.070
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: Qualitative
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0390.057
Scholarly communication0.0170.007
Open science0.0040.016
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0020.000

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 head0.105
GPT teacher head0.416
Teacher spread0.311 · 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
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSage Journals DataFrench-language works237,207