Bibliographic record
Abstract
Each year thousands of individuals are injured on the job.With effective medical treatment, musculoskeletal injuries heal and most individuals returned to• work; however, some do not make the transition from medical treatment back to work.Through selfefficacy theory Bandura' s ( 1977) postulates that individuals maintain considerable influence over the outcome of their experience based on their self-perceptions.However, research is lacking on whether individuals with higher levels of work self-efficacy are more likely to return to work sooner following an injury than individuals with lower levels of work self-efficacy.Therefore, through this exploratory research, the Return to Work Self-Efficacy Scale was developed and tested through in-person interviews with a convenience sample of 19 participants who were injured on the job; were in receipt of Workers' Compensation benefits; and were attending their final treatment at an Occupation Rehabilitation Program, Canadian Back Institute.Analysis revealed that individuals with a high level of work self-efficacy, also presented with a high level of coping with pain self-efficacy, a high level of physical function self-efficacy, a high level of coping with symptoms self-efficacy.These individuals were more likely to return to work following injury than their lower level of self-efficacy counterparts.The research concludes with recommendations for further study.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".