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Record W1531561007 · doi:10.3233/wor-2007-00652

Unexpected barriers in return to work: Lessons learned from injured worker peer support groups

2007· article· en· W1531561007 on OpenAlexaffabout
Ellen MacEachen, Agnieszka Kosny, Sue Ferrier

Bibliographic record

VenueWork · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsPeer supportSocial supportWork (physics)Compensation (psychology)Social workPsychologyQualitative researchWorkers' compensationPeer groupNursingCompliance (psychology)BusinessMedicineSocial psychologyPolitical scienceSociologyEngineering

Abstract

fetched live from OpenAlex

Some workers who are injured at work have unexpectedly prolonged absences from work. Experiences of workers who constitute a disproportionate cost to the return-to-work system and the systemic and compliance-related barriers they encounter during the process of returning to work are reported. A qualitative interview based study was conducted with 37 members of three injured worker peer support groups in a Canadian province. Four dimensions of peer support were identified: worker experience of being misunderstood by system providers, need for advocates, social support, help with procedural complexities of the workers' compensation, and health care systems. Peer support constitutes a partial return-to-work solution for workers with injuries, but injured workers encounter an uneven playing field. Injured worker peer support group needs and activities show us that sensitivity to structural and social issues may lead to better return-to-work outcomes.

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)0.031
metaresearch head score (Gemma)0.060
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0110.009
Scholarly communication0.0070.009
Open science0.0060.009
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.129
GPT teacher head0.484
Teacher spread0.354 · 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

Citations66
Published2007
Admission routes2
Has abstractyes

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