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Record W2146673137 · doi:10.1177/1524839908324779

Hidden Heroines: Lone Mothers Assessing Community Health Using Photovoice

2008· article· en· W2146673137 on OpenAlexaffabout
Lynne Duffy

Bibliographic record

VenueHealth Promotion Practice · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhotovoiceParticipatory action researchPublic healthContext (archaeology)Community-based participatory researchHealth promotionAgency (philosophy)SociologyPublic relationsGeneral partnershipPsychologyGender studiesNursingPolitical scienceMedicineEconomic growthSocial scienceGeography

Abstract

fetched live from OpenAlex

Between 2005 and 2007, a small group of lone mothers in Moncton, New Brunswick, carried out participatory action research within a university-community agency partnership. Applying the method of photovoice, the women took pictures within their community context on topics that they considered important to their health, health promotion, and quality of life. Eight themes that emerged from the process were represented with pictures and captions and presented in numerous public venues and conferences. Themes included finances, stress, support, personal development, violence and abuse, place, and transportation. The visual images and accompanying captions bring to the public arena the voices of those who are often most affected by public policy but have little, if any, input into its creation. Nurses and other health professionals can play a critical role in working toward gender and economic justice, while accompanying marginalized populations in ways that respect their beliefs, perceptions, and experiences.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.852
GPT teacher head0.719
Teacher spread0.133 · 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

Citations26
Published2008
Admission routes2
Has abstractyes

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