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Record W3125606921 · doi:10.1111/nhs.12816

Mental health workers' experiences of support and <scp>help‐seeking</scp> following workplace violence: A qualitative study

2021· article· en· W3125606921 on OpenAlexafffund
Nicole C. Rodrigues, Elke Ham, Bonnie Kirsh, Michael C. Seto, N. Zoe Hilton

Bibliographic record

VenueNursing and Health Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of TorontoWaypoint Centre for Mental Health Care3M (Canada)Royal Ottawa Mental Health Centre
FundersWorkSafeBC
KeywordsMental healthHelp-seekingAnxietyQualitative researchPeer supportPsychologyBurnoutConfidentialityStigma (botany)Occupational safety and healthPsychiatryNursingClinical psychologyMedicine

Abstract

fetched live from OpenAlex

The consequences of workplace trauma among mental health staff can include physical injuries and somatic disorders, professional exhaustion and burnout, depression, anxiety, and other occupational stress injuries. For the well-being of staff and patients, there is a need to understand mental health workers' experiences following exposure to workplace trauma, any subsequent mental health problems, and the process of help-seeking. The nuances of these experiences can best be captured through qualitative exploration. In this study, we explored inpatient mental health workers' experiences of support and help-seeking following workplace violence. Four overall themes emerged from interviews with 12 participants: (i) validation as motivation for help-seeking; (ii) stigma as a barrier to help-seeking; (iii) gaps in services provided; and (iv) desire for accessible and effective trauma support and education. This study demonstrates the need for supportive management responses and peer support, access to specialized and confidential trauma-informed mental health services, and reductions in stigma, victim blaming, and other barriers to help-seeking among mental health workers.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.443
Teacher spread0.386 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations32
Published2021
Admission routes2
Has abstractyes

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