PSPNET Families Wellbeing Hub: A preliminary evaluation of online upstream supports for public safety personnel families
Bibliographic record
Abstract
Public safety personnel (PSP) face diverse challenges (e.g., trauma exposure, unscheduled call-ins, nonstandard work hours) that can spill over into home lives. While the challenges of PSP family life have been acknowledged, resources that address the daily experiences of PSP family members are limited. A growing body of evidence shows the positive outcomes of virtual/digital resources and interventions to support mental health and well-being. Accordingly, this Canadian research team, funded by the Public Health Agency of Canada, set out to shape the PSPNET Families Wellbeing Hub, an online ecosystem of upstream mental health and well-being information, skill-building, and self-directed cognitive-behavioural therapy supports designed to address the needs and contexts of PSP family life. The full site launched in English on Dec. 5, 2022. This article draws on both qualitative and quantitative preliminary data from three data sources collected in early 2023 - Google Analytics, social media responses, and semi-structured interviews - to understand the degree to which the site resonates with the population and identify opportunities for growth. Results showed a need for PSPNET Families but also that further data collection is required to fully assess the extent to which the site is responding to, and benefiting, PSP families.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".