MétaCan
Menu
Back to cohort
Record W2055678321 · doi:10.1177/1359105310371703

Creating New Career Pathways to Reduce Poverty, Illiteracy and Health Risks, while Transforming and Empowering Cambodian Women’s Lives

2010· article· en· W2055678321 on OpenAlexaff
Helen Lee, Gabe Pollock, Ian Lubek, Stacy Niemi, Katie O'Brien, Michelle Green, Sabina Bashir, Ellyn Braun, Sarath Kros, Virakboth Huot, Vanna Ma, Neela Griffiths, Brett G. Dickson, Noeun Pring, Kris Sphkurst Huon-Ribeil, Natalie Lim, Jasmin K. Turner, Chris Winkler, Mee Lian Wong, Tiny Van Merode, Bun Cheem Dy, Sophiap Prem, Roel Idema

Bibliographic record

VenueJournal of Health Psychology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFunctional illiteracyEmpowermentPovertyParticipatory action researchPsychologySociologyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Community health psychology provides a framework for local citizens themselves to systematically affect change in health and social inequalities, particularly through Participatory Action Research (PAR). The Cambodian NGO SiRCHESI launched a 24-month Hotel Apprenticeship Program (HAP) in 2006 to provide literacy, English, social skills, health education, hotel skills-training, work experience and a living wage to women formerly selling beer in restaurants; there they had faced workplace risks including HIV/AIDS, alcohol overuse, violence and sexual coercion. Quantitative and qualitative analyses indicate changes in health-related knowledge, behaviour, self-image and empowerment, as HAP trainees were monitored and evaluated within their new career trajectories.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.568
GPT teacher head0.651
Teacher spread0.082 · 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

Citations24
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Health PsychologySame topicParticipatory Visual Research MethodsFrench-language works237,207