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Record W4404326729 · doi:10.70256/575573kvstfh

Sharing the Trauma

2010· article· en· W4404326729 on OpenAlexaboutno aff
Heidi Heft LaPorte, Jay Sweifach, Norman Linzer

Bibliographic record

VenueBest practices in mental health · 2010
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceGeology

Abstract

fetched live from OpenAlex

This article highlights the results of a qualitative study exploring the perceptions of front-line social workers about the use of therapist self-disclosure (TSD) to work through painful emotional distress subsequent to the disaster when working with clients in the aftermath of a catastrophic event. This study was conducted with focus groups of social workers in health-care and social-service settings in the United States, Canada, and Israel. Transcripts were coded for themes and analyzed using the computer software package Atlas TI. The respondents identified a number of ethical and practical dilemmas. Prominent among them was the use of TSD following a shared traumatic event. In this article, the discussion focuses on circumstances surrounding the use of TSD by practitioners during and immediately following disaster-based emergencies, such as the SARS epidemic in Canada, 9/11, ongoing terrorist acts in Israel, and hurricanes in Florida. This article presents findings specific to TSD. These findings are part of a larger study that examined the impact of man-made and natural disasters on social work practice. The findings reveal a beginning understanding about how and the extent to which TSD is and is not used in the therapeutic relationship when both client and therapist have experienced simultaneous trauma. We also offer a beginning set of recommendations for using TSD with disaster victims.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.107
GPT teacher head0.478
Teacher spread0.370 · 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
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

Citations2
Published2010
Admission routes1
Has abstractyes

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