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Record W4412686064 · doi:10.3389/frhs.2025.1663204

Editorial: Mental health services for occupational trauma: decreasing stigma and increasing access, volume 2

2025· editorial· en· W4412686064 on OpenAlexaff
Warren N. Ponder, Natalie Mota, Shay‐Lee Bolton

Bibliographic record

VenueFrontiers in Health Services · 2025
Typeeditorial
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStigma (botany)Mental healthPsychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

burnout significantly mediated the relationship between work and family conflict and job satisfaction, but that social support moderated the impact of burnout on job satisfaction. This finding underscores the importance of a supportive relationship at work and at home in mitigating the deleterious effects of burnout. Social support is also key to other frontline healthcare professions, such as nursing. In a systematic review and meta-analysis, Chen et al. (2024) found an inverse relationship between social support and turnover intention, a measure assessing the likelihood that they would leave their jobs. These results may be useful as a guide for nurse managers, healthcare centers, and policy administrators with actionable items to help reduce turnover, by encouraging and promoting social support. Sim et al. (2024) used a sample of South Korean nurses to examine posttraumatic growth (PTG), burnout, and posttraumatic stress disorder (PTSD) during the COVID-19 pandemic. They Ponder et al. Editorial,2 found that purposeful rumination, emotional expression and cognitive emotional regulation (cognitive coping while not being overburdened by negative emotions), increased PTG. Furthermore, they showed that PTG was a protective factor both against burnout and persistent PTSD symptoms. Melander et al. (2024) investigated social support and PTSD in a sample of Danish ambulance personnel and found that social support predicted higher levels of PTSD symptoms, and that informal managerial and collegial support was preferential to formal social support (e.g., debriefing/defusing, formal training for peer support or a manager). Their sample also overwhelmingly preferred seeking out a family member or close friend for support. These studies further illustrated the importance of social support and protective factors against burnout.A sizable minority of U.S. first responders-between 17% and 28%-have prior military service (Baker et al., 2023b;Ponder et al., 2023), and there may be additional institutional (e.g., sensitivity, logistic, and not fitting in) and stigma-related barriers to care that should be considered (Ouimette et al., 2011). Ein et al. (2024) conducted a rapid review to understand barriers and facilitators related to mental health service utilization in veterans. Some examples of primary barriers included system navigation difficulties and negative attitudes toward mental health, while facilitators included mental health literacy and social support. If this population can overcome perceptions of stigma and potential negative impacts on career trajectory, recent research has recommended a transdiagnostic approach that focuses on emotion regulation (Schman et al., 2025). To help break down barriers to care, Meyer et al. (2025) sought to address stigma, logistical barriers, and lack of therapist cultural competency through implementation of the Unified Protocol in a sample of first responders. They found significant reductions in PTSD, depression, and generalized anxiety symptoms among first responders in this uncontrolled trial with treatment delivered by telehealth (Meyer et al., 2025). Ponder et al. Editorial,3 While this special issue fills some of the gaps in the literature, much more can be done.One of the most concerning consequences of burnout and untreated mental health symptoms is an increased risk of substance misuse as a coping mechanism, which can lead to serious career repercussions for members in these occupational roles, including legal consequences. To address this, Fort Worth, Texas created the first Public Safety Employees Treatment Court (PSETC), which gives first responders an opportunity for participation in a specialty diversion court program that, if successfully completed, could dismiss their case. The program typically takes 8to 24-months, and the participants have to adhere to an agreed upon collaborative treatment plan established at entrance into the program. In the initial study, there were reductions in suicidality, generalized anxiety, depression, emotional distress, and PTSD, while resilience was increased (Ponder et al., 2025).It is also important to continue to understand risk and protective factors for burnout and mental health in these populations by conducting additional international epidemiological studies. We propose that an interdisciplinary team of scholars and data analysts should leverage international samples using the same assessments for secondary data analytic comparative studies. This taskforce could function in a similar manner to what the National Vietnam Veterans Readjustment Study achieved for Vietnam veterans in the 1980s (Kulka et al., 1990). Using a nationally representative sample, findings from that study elucidated the scale of mental health problems among veterans and, in 1989, led to the first VA-established National Center for PTSD in Boston (Friedman, 2012). Since this time, the National Center has been at the forefront of the continued study of trauma in veterans and evidence-based solutions to alleviate suffering from posttraumatic stress. First responders deserve the same level of scholarly investigation. We hope this special issue contributes to a larger body of much-needed work in this area.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.379
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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