Return-to-work: The importance of human interactions and organizational structures
Bibliographic record
Abstract
The purpose of this study was to gain insight into stakeholder perspectives on barriers and facilitators for return-to-work (RTW). Qualitative methodology with purposive sampling was employed. A total of 55 participants, representing a wide spectrum of stakeholders and industry, were interviewed in individual or group format. Interview transcripts were coded, categorized according to themes, and placed within a framework which reflected the dynamic interaction of individuals and the structural systems or context of those individuals. Findings indicated that perceived barriers to RTW included delays of all types in processing or delivery of information or treatment, and ineffective communication among stakeholders. Facilitators to RTW included establishment of RTW programs in the workplace, effective communication and teamwork, as well as trust and credibility among stakeholders. The interdependence of organizational structures and human interactions was evident in successful RTW programs which emphasized teamwork, early intervention, and communication. Differing stakeholder perspectives, however, especially on issues such as worker attitudes and participation, must be acknowledged and addressed if more injured workers are to be successful in returning to full employment.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".