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Record W7071381253

'Revisiting the debriefing debate: does psychological debriefing reduce PTSD symptomology following work-related trauma? A meta-analysis' and 'Assessing alexithymia in forensic practice: psychometric properties of the 20-item Toronto Alexithymia Scale and the Perth Alexithymia Scale in a sample of adult male prisoners'

2022· dissertation· en· W7071381253 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Birmingham Institutional Research Archive (University of Birmingham) · 2022
Typedissertation
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsDebriefingAlexithymiaToronto Alexithymia ScaleConstruct (python library)Scale (ratio)Intervention (counseling)Feeling
DOInot available

Abstract

fetched live from OpenAlex

LITERATURE REVIEW: Psychological debriefing is an early post-trauma intervention which aims to prevent the development of PTSD and accelerate normal recovery through discussing, validating, and normalising group members responses to trauma. While originally designed in the 1980s for groups of emergency service personnel, the scope of psychological debriefing extended to individual primary victims of trauma. A Cochrane review in 2002 concluded that psychological debriefing was ineffective, yet some authors have argued that many of the studies that informed the Cochrane review did not adhere to key elements of psychological debriefing. This meta-analysis sought to re-examine the effectiveness of psychological debriefing in preventing or reducing PTSD symptoms following work-related trauma. Appropriate studies were selected from three databases (MEDLINE, Embase and PsycINFO). Inclusion criteria was intentionally broad so that features of psychological debriefing that may determine its effectiveness could be explored through a series of subgroup analyses. The overall synthesis did not find consistent evidence that psychological debriefing helps to prevent or reduce PTSD symptoms following work-related trauma. Shortcomings in the methodology and reporting of many of the studies meant that several important subgroup analyses could not be conducted. Further well-designed studies in this field are warranted to ensure that employees exposed to potentially traumatic events receive the effective support they need and deserve. \n \nEMPIRICAL PAPER: Alexithymia is a trans-diagnostic construct comprised of a cluster of difficulties relating to emotional processing: difficulties identifying feelings, difficulties describing feelings and externally oriented thinking. Alexithymia is positively associated with several risk factors for offending behaviour and higher levels of alexithymia have been found in violent offenders. Consequently, it has become an area of interest in criminological research and there have been recommendations to consider alexithymia in risk assessments and interventions among violent offenders. This study sought to test and compare the psychometric properties of two measures of alexithymia in a forensic sample: the well-established 20-item Toronto Alexithymia Scale (TAS-20) and the recently developed Perth Alexithymia Questionnaire (PAQ). The internal consistency, convergent validity, and factorial validity of both the TAS-20 and PAQ were examined using a sample of 78 adult male prisoners in the UK. Both measures were found to assess a similar multifaceted alexithymia construct. However, the externally oriented thinking subscale of the TAS-20 had poor internal consistency and factor loadings. Conversely, the PAQ was found to have robust psychological properties across all subscales, while also offering the added benefit of valence-specific measurement. These findings provide preliminary support for the PAQ as a measure of alexithymia within both forensic practice and research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.079
metaresearch head score (Gemma)0.212
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.420

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.212
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.025
Bibliometrics0.0100.007
Science and technology studies0.0010.002
Scholarly communication0.0080.006
Open science0.0040.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.345
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
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

Citations0
Published2022
Admission routes1
Has abstractyes

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