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Record W1985360542 · doi:10.3109/09638288.2011.573056

Biographical disruption of injured workers in chronic pain

2011· article· en· W1985360542 on OpenAlexaff
Sophie Soklaridis, Carrie Cartmill, D Cassidy

Bibliographic record

VenueDisability and Rehabilitation · 2011
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity Health NetworkSt. Michael's Hospital
Fundersnot available
KeywordsGrounded theoryRehabilitationFocus groupQualitative researchPsychologyMeaning (existential)Psychology of selfChronic painIdentity (music)Social identity theorySocial psychologySociologyPsychotherapistSocial groupPsychiatryAesthetics

Abstract

fetched live from OpenAlex

PURPOSE: This research explored how injured workers living with work-related chronic pain rethink and reconstruct their biographical experience. METHOD: This qualitative study used a grounded theory approach to data collection and analysis. Semi-structured focus groups were conducted to gather data and analysis was performed by the coding of emergent themes. RESULTS: Analysis of the focus groups revealed the impact that chronic pain has on the social components of an injured worker's life; particularly their sense of self, their relationship to others and how they perceive themselves in social situations. CONCLUSIONS: Injured workers experienced changes (physical, psychological and social transformations) that led to biographical disruption; a change in self-identity, which in turn contributed to changes in important relationship dynamics. Injured workers spoke of repeated losses - loss of self, relationships and of the life imagined. Understanding the meaning of these losses could improve the conditions surrounding the injured worker's biographical reconstruction and facilitate the rehabilitation process.

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)0.012
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.006
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.002
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 head0.014
GPT teacher head0.282
Teacher spread0.267 · 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

Citations20
Published2011
Admission routes1
Has abstractyes

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