MétaCan
Menu
Back to cohort
Record W1705195348 · doi:10.15446/rsap.v16n1.35812

Perfil de estrés y estrés crónico en migrantes mexicanos en Canadá

2014· article· es· W1705195348 on OpenAlexaboutno aff
Blanca Elizabeth Pozos Radillo, Manuel Pando, María de los Ángeles Aguilera Velasco, Martín Acosta Fernández

Bibliographic record

VenueRevista de Salud Pública · 2014
Typearticle
Languagees
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPopulationChronic stressDemographyMedicineClinical psychologyEnvironmental healthSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: Establishing an association between high chronic stress levels and variables considered to be negative regarding the stress profile for Mexican migrants living in Edmonton, Canada. METHODS: A simple random technique was used for choosing the target population; the sample size involved 58 migrants. The Nowack Stress Profile and Maslach Burnout Inventory were used to identify immigrants' stress symptoms during 2010-2011. RESULTS: Chronic stress levels were classified as being 24 % high, 45 % medium and 21 % low. Statistical regression analysis determined that a stressful situation and threat minimisation were predictors for developing high levels of chronic stress. CONCLUSIONS: Stress situation and threat minimisation were predictors for developing high levels of chronic stress; migrant women (unlike males) tended not to use threat minimisation to deal with stress.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.058
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.342
Teacher spread0.329 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueRevista de Salud PúblicaSame topicStress and Burnout ResearchFrench-language works237,207