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Record W1995349713 · doi:10.1207/s15327655jchn1702_6

Health Promotion and Senior Women With Limited Incomes

2000· article· en· W1995349713 on OpenAlexaffabout
Heather Morris, Janet C. Kerr, Marilynn J. Wood, Margaret Haughey

Bibliographic record

VenueJournal of Community Health Nursing · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPromotion (chess)NursingMedicineHealth promotionFamily medicineBusinessGerontologyPsychologyPolitical sciencePublic health

Abstract

fetched live from OpenAlex

Health promotion is increasingly being recognized as making an important contribution to the well-being of Canada's seniors. Most research relating to this topic, however, has focused on middle-income senior men and women. An exploratory study using ethnographic methods was conducted to explore and describe the health promotion experience of senior women living on limited incomes. Interviews with a total of 11 urban senior women living on limited incomes were analyzed. A major finding of this study was that the women utilized a wide variety of "ways of living" that are presented in the model, Health Promotion as Self Nurturance. Health promotion was perceived to be influenced by living on a limited income by most participants; however, 3 of the participants believed that their health status and income level were unrelated. Findings are discussed and implications for community health nurses are offered.

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)0.004
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.046
GPT teacher head0.394
Teacher spread0.348 · 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

Citations14
Published2000
Admission routes2
Has abstractyes

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