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Record W2109026303 · doi:10.1080/00207594.2010.532799

Coping with traumatic stress in journalism: A critical ethnographic study

2011· article· en· W2109026303 on OpenAlexaffabout
Marla J. Buchanan, Patrice A. Keats

Bibliographic record

VenueInternational Journal of Psychology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsWitnessCoping (psychology)PsychologyEthnographyJournalismPopulationSocial psychologyApplied psychologyClinical psychologyMedia studiesMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Journalists who witness trauma and disaster events are at risk for physical, emotional, and psychological injury. The purpose of this paper is to present the results of a critical ethnographic study among 31 Canadian journalists and photojournalists with regard to coping strategies used to buffer the effects of being exposed to trauma and disaster events and work-related stress. The findings are the result of in-depth individual interviews and six workplace observations with journalists across Canada. The most commonly reported coping strategies were: avoidance strategies at work, use of black humor, controlling one's emotions and memories, exercise and other physical activities, focusing on the technical aspects, and using substances. Recommendations for addressing the effects of work-related stress within this population are provided.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0200.009
Scholarly communication0.0050.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.102
GPT teacher head0.449
Teacher spread0.347 · 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.

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

Citations123
Published2011
Admission routes2
Has abstractyes

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