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Record W3004086817 · doi:10.1080/09638237.2020.1714012

A qualitative inquiry into psychiatrists’ perspectives on the relationship of psychological trauma to mental illness and treatment: implications for trauma-informed care

2020· article· en· W3004086817 on OpenAlexaff
Sophie Isobel, Brenda Gladstone, Melinda Goodyear, Trentham Furness, Kim Foster

Bibliographic record

VenueJournal of Mental Health · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMental illnessMental healthQualitative researchPsychological traumaPsychiatryTherapeutic relationshipPsychologyMedicinePsychotherapistClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Trauma is a factor impacting the lives of many people experiencing psychiatric disorders. Trauma affects people's responses to illness as well as their interactions with services. AIM: This study aimed to explore the understandings and experiences of psychiatrists of working with trauma and emerging models of Trauma-Informed Care. METHODS: An interpretive qualitative inquiry was undertaken using semi-structured in-depth interviews with psychiatrists. RESULTS: Four themes were identified: Making sense of trauma; A contentious relationship between trauma and mental illness; Treatment made more challenging by trauma; Trauma-Informed Care highlights tensions. Psychiatrists are familiar with the concept of trauma but there are differences in beliefs about its relationship to mental illness that are consequential for practice. Trauma-Informed Care is seen as an effort to humanise mental health services, but with perceived limited impact on psychiatrists' roles. CONCLUSION: Findings indicate need for further consultation and collaboration with psychiatrists around trauma-informed care implementation; as well as consideration of what is required to develop professional consensus on trauma and its relationship to illness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.199
GPT teacher head0.526
Teacher spread0.327 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2020
Admission routes1
Has abstractyes

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