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Record W2126485241 · doi:10.1002/jts.21715

Mexican journalists: An investigation of their emotional health

2012· article· en· W2126485241 on OpenAlexaff
Anthony Feinstein

Bibliographic record

VenueJournal of Traumatic Stress · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntimidationSuicide preventionDrugOccupational safety and healthMental healthArousalPsychiatryPsychologyPoison controlMedicineSocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

Mexican journalists are frequently the victims of violence, often drug related. The purpose of the study was to assess their mental well-being. Of 104 journalists recruited from 3 news organizations, those who had stopped working on drug-related stories because of intimidation from the criminal drug cartels (n = 26) had significantly greater social dysfunction (p = .024); and more depressive (p = .001) and higher intrusive (p = .027), avoidance (p = .005), and arousal (p = .033) symptoms than journalists living and working under threat in regions of drug violence (n = 61). They also had more arousal (p = .05) and depressive (p = .027) symptoms than journalists (n = 17) never threatened before and living in regions without a drug problem. These findings provide preliminary data on the deleterious effects of drug-related violence on the Mexican media, amplifying the concerns expressed by journalist watchdog organizations monitoring the state of the press in the country.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.329
Teacher spread0.187 · 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 designObservational
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

Citations57
Published2012
Admission routes1
Has abstractyes

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