A qualitative system review: To lead public policy advancement for workplace psychological violence injury
Bibliographic record
Abstract
This article presents a qualitative system analysis informing public policy advancement for workplace psychological violence injury. It is a unique look into how a jurisdiction responds to the injury caused by workplace psychological violence (workplace bullying, harassment, and discrimination campaigns). First ever provincial legislation addressing this harm was passed into law. This study found that the legislation has yet to be implemented into the medical response systems dealing with this workplace injury. Severe hardships and consequences are experienced by workers in attempting recovery. Some never get there. The Canadian Institute for Workplace Bullying Resources, a specialist with this injury, received this study's recommendations for system change to assist in closing gaps and reduce the harm experienced by workers in Alberta, Canada. This study found some causal factors to these system barriers to be common globally. Methods to map and lead advocacy system change through qualitative research for this workplace violence and caused injury are provided herein. This is a call to researchers and professionals globally who deal with both workplace psychological violence and injury, to use and build on this study's methods, tools, and outcomes to lead further needed system change.
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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 | 0.363 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".