Including the ergonomist's voice in integrating MSD prevention and psychological health and safety: Challenges, tools, and considerations
Bibliographic record
Abstract
The purpose of this study was to gather ergonomists' perspectives and experiences, describing current challenges and contextual considerations in risk assessment, exploring how ergonomists are currently integrating the multiple domains of ergonomics for MSD and/or psychological health and safety and highlighting key considerations in the design and format of future tools. In-depth, semi-structured interviews were conducted with twenty Canadian ergonomists to explore risk assessment tool use, favoured characteristics and format of tools, commonly addressed risk factors in their practice, and tools relating to both MSD prevention and psychological health and safety. The range of practitioner years of experience highlighted differing needs and approaches to the use and formatting of risk assessment tools. Practitioners reported using quantitative outcomes (levels of risk, values) from traditional physical tools complemented by a general observation of psychosocial or organizational factors. Though many respondents had not yet encountered the need for psychological injury assessment in their sectors it was identified as a quickly emerging area citing a need for valid and reliable tools. Practitioners noted a lack of available tools that integrated cognitive and psychosocial items presenting a future challenge for integrated tools that covered multiple ergonomic domains. Along with recommendations for future tool development, the authors reflect on the process of qualitative inquiry as an essential step in the risk assessment process. Future studies will be needed to develop and evaluate measurement properties of integrating psychosocial factors and their respective tools in traditional MSD assessment.
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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 | 0.120 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".