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Record W1967362910 · doi:10.1177/000841740607300304

Exploring the Mental Health Needs of Injured Workers

2006· article· en· W1967362910 on OpenAlexafffundvenueabout
Lucia Cacciacarro, Bonnie Kirsh

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of TorontoWilliam Osler Health System
FundersWorkplace Safety and Insurance Board
KeywordsMental healthOccupational therapyPsychologyNursingMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The mental health of injured workers has been relatively unexplored in the literature, though there is a suggestion that needs are substantive. PURPOSE: This study explored the experiences of injured workers to generate an understanding of their mental health needs. METHODS: In-depth qualitative interviews were carried out on a purposeful sample of 4 injured workers living in the greater Toronto area. Data was analyzed inductively and four major themes emerged. RESULTS: Themes related to the life changes that result from work injury, and the sense of alienation from society and abandonment by the compensation system. Injured workers reflected that continued involvement in meaningful occupations and encouragement from supportive others helped to promote positive well-being after the injury. All participants emphasized the need for systemic change. PRACTICE IMPLICATIONS: Occupational therapists and other professionals working with the injured worker population can use findings from this study to promote positive mental health and well-being among injured workers and their families.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.384
GPT teacher head0.499
Teacher spread0.116 · 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

Citations33
Published2006
Admission routes4
Has abstractyes

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