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Record W4319841955 · doi:10.1177/1097184x221149984

“I’m Trying to Be There for My Kids”: A Needs Analysis of Fathers Who Experience Health Inequities in Vancouver, Canada

2023· article· en· W4319841955 on OpenAlexafffundabout
Francine Darroch, John L. Oliffe, Gabriela Gonzalez Montaner, Jessica M. Webb

Bibliographic record

VenueMen and Masculinities · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of British ColumbiaCarleton University
FundersBanting Research Foundation
KeywordsDisadvantagedThematic analysisService providerHealth promotionDowntownPromotion (chess)SociologyWork (physics)Service (business)PsychologyQualitative researchGerontologyGender studiesNursingMedicinePublic healthEconomic growthPolitical scienceSocial scienceBusinessPolitics

Abstract

fetched live from OpenAlex

To better understand the needs of fathers who experience health inequities, we individually interviewed fathers, mothers, and service providers about their perspectives of supports for men in Vancouver’s Downtown Eastside, one of the most disadvantaged groups in Canada. Using a gender lens, thematic analysis of transcribed interviews with three cohorts revealed the following themes: “we need a He-way”: Fathers arguing for men-friendly services; “I had to do all the hard work”: Mothers identifying relational impacts of fathers’ barriers to services; “there is nothing out there for them”: Service providers acknowledging the lack of father-focused programs. Findings highlight the need for, and challenges to creating accessible, gender specific, father focused programs and services to best support men and families within the complex contexts of experiencing significant health inequities. This work illustrates how gender-based analyses can guide strategies for health promotion programs that will ultimately support fathers, mothers, and their families.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.165
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0240.004
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.065
GPT teacher head0.336
Teacher spread0.271 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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