Barriers for implementation of successful change to prevent musculoskeletal disorders and how to systematically address them
Bibliographic record
Abstract
This scoping review identified common barriers and facilitators encountered during the implementation of changes to prevent musculoskeletal disorders (MSD) and examined their relationship with those encountered in general Occupational Health and Safety (OHS) efforts. Thematic analysis of the literature identified 11 barriers: (i) Lack of time; (ii) Lack of resources; (iii) Lack of communication; (iv) Lack of management support, commitment, and participation; (v) Lack of knowledge and training; (vi) Resistance to change; (vii) Changing work environment; (viii) Scope of activities; (ix) Lack of trust, fear of job loss, or loss of authority; (x) Process deficiencies; and (xi) Difficulty of implementing controls. Three facilitators identified were: (i) Training, knowledge and ergonomists' support; (ii) Communication, participation and support; and (iii) An effective implementation process. The barriers and facilitators identified were similar to those in general OHS processes. The integration of MSD prevention into a general management system approach may overcome these barriers.
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 | 0.052 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".