Applying the Theoretical Domains Framework to identify police, fire, and paramedic preferences for accessing mental health care in a First Responder Operational Stress Injury Clinic: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: First responders and other public safety personnel (PSP; e.g. correctional workers, firefighters, paramedics, police, public safety communicators) are often exposed to events that have the potential to be psychologically traumatizing. Such exposures may contribute to poor mental health outcomes and a greater need to seek mental health care. However, a theoretically driven, structured qualitative study of barriers and facilitators of help-seeking behaviours has not yet been undertaken in this population. This study used the Theoretical Domains Framework (TDF) to identify and better understand critical barriers and facilitators of help-seeking and accessing mental health care for a planned First Responder Operational Stress Injury (OSI) clinic. METHODS: We conducted face-to-face, one-on-one semistructured interviews with 24 first responders (11 firefighters, five paramedics, and eight police officers), recruited using purposive and snowball sampling. Interviews were analyzed using deductive content analysis. The TDF guided study design, interview content, data collection, and analysis. RESULTS: The most reported barriers included concerns regarding confidentiality, lack of trust, cultural competency of clinicians, lack of clarity about the availability and accessibility of services, and stigma within first responder organizations. Key themes influencing help-seeking were classified into six of the TDF's 14 theoretical domains: environmental context and resources; knowledge; social influences; social/professional role and identity; emotion; and beliefs about consequences. CONCLUSION: The results identified key actions that can be utilized to tailor interventions to encourage attendance at a First Responder OSI Clinic. Such approaches include providing transparency around confidentiality, policies to ensure greater cultural competency in all clinic staff, and clear descriptions of how to access care; routinely involving families; and addressing stigma.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".