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Record W4387451284 · doi:10.1080/21679169.2023.2262518

Role of Bio-Psychosocial factors in return to work following a compensable knee injury

2023· article· en· W4387451284 on OpenAlexaff
Alicia Savona, Helen Razmjou

Bibliographic record

VenueEuropean Journal of Physiotherapy · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsPsychosocialAnxietyMedicineDepression (economics)ReferralRehabilitationCross-sectional studyPhysical therapyPsychologyClinical psychologyPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

Objectives The purpose of this study was to examine the relationship between work status and biomedical factors, psychosocial factors, occupational factors, physical disability, and barriers to full recovery.Methods This study was a cross sectional evaluation of workers with an active compensation claim following a work-related injury to the knee joint.Results Data of 60 consecutive injured workers, mean age, 47 ± 14, 40 (67%) males were used for analysis. Thirty-one (52%) patients were unable to work and 29 (48%) patients were working at the time of assessment. Patient’s age, gender, range of motion, and strength did not have a direct relationship with work status (p > 0.05). Obesity had a negative impact on work status (p = 0.035). Depression (p = 0.001), anxiety (p = 0.002), and the fear avoidance of the Optimal Screening for Prediction of Referral and Outcome (OSPRO-YF) scale (p = 0.019) showed higher levels of psychosocial issues in the non-working sample. Patients with 3 or more barriers (p = 0.020) and higher disability score (p = 0.004) showed an inferior work status.Discussion Presence of obesity, depression, anxiety, fear-related beliefs, higher reported disability, and accumulative number of barriers are indicative of poorer recovery and a less successful RTW after an occupational knee injury.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

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