The Impact of Organizational Culture on the Mental Well-being of Public Safety Communicators
Bibliographic record
Abstract
Public safety communicators (e.g., 9-1-1, police, fire, and ambulance call-takers and dispatchers), like many other public safety personnel (e.g., paramedics), suffer operational stress injuries (OSIs). However, there is very little data on OSI prevalence among Canadian communicators, and to our knowledge, this is the only pan-Canadian study focusing on organizational culture and its potential influence on OSIs within the communicator context. In the current qualitative study, we focus on participant responses (n = 329) to an online survey that revealed communicator OSIs are impacted by organizational and operational factors (work environment, e.g., feeling undervalued by one’s organization), and interactions with others (interpersonal work relationships; e.g., management). A semi-grounded thematic approach was used to analyse how communicators described the organizational culture in their communications centres. Six dominant themes emerged: perceptions of organization, management and supervision, morale and staffing, division and exclusion, colleagues, and gender. The findings suggest that, while organizational culture is a key factor in employee well-being, it varies considerably across agencies. These findings on organizational culture’s role in OSIs may help reduce the frequency and severity of communicator OSIs, helping ensure that emergency services are delivered to Canadians.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".