Development of an Evidence-Informed Solution to Emotional Distress in Public Safety Personnel and Healthcare Workers: The Social Support, Tracking Distress, Education, and Discussion CommunitY (STEADY) Program
Bibliographic record
Abstract
Public safety personnel (PSP) and healthcare workers (HCWs) are frequently exposed to traumatic events and experience an increased rate of adverse mental health outcomes compared to the public. Some organizations have implemented wellness programming to mitigate this issue. To our knowledge, no programs were developed collaboratively by researchers and knowledge users considering knowledge translation and implementation science frameworks to include all evidence-informed elements of posttraumatic stress prevention. The Social Support, Tracking Distress, Education, and Discussion Community (STEADY) Program was developed to fill this gap. It includes (1) peer partnering; (2) distress tracking; (3) psychoeducation; (4) peer support groups and voluntary psychological debriefing following critical incidents; (5) community-building activities. This paper reports on the narrative literature review that framed the development of the STEADY framework and introduces its key elements. If successful, STEADY has the potential to improve the mental well-being of PSP and HCWs across Canada and internationally.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".