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Record W4406920598 · doi:10.1177/08445621251313500

“ <i>I Am Not Alone”</i> : A Photovoice Exploration of Diabetes Self-Management for Older Persons in Rural Ontario, Canada

2025· article· en· W4406920598 on OpenAlexafffundvenueabout
Lenora Duhn, Madison Robertson, Idevânia G. Costa, Beatriz Alvarado, Geneviève C. Paré, Pilar Camargo‐Plazas

Bibliographic record

VenueCanadian Journal of Nursing Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsLakehead UniversityQueen's University
FundersInstitute of Aging
KeywordsPhotovoicePsychological interventionContext (archaeology)Diabetes managementGerontologySelf-managementQualitative researchMental healthParticipatory action researchPsychologyNursingMedicineDiabetes mellitusType 2 diabetesSociologyPsychotherapist

Abstract

fetched live from OpenAlex

ObjectiveTo explore diabetes self-management for older adults in rural Ontario.MethodsFourteen adults, 65 and older, with diabetes, participated in this study using a participatory, art-based approach involving photovoice and semi-structured interviews. Data underwent hermeneutic phenomenology analysis.FindingsFour themes emerged, elucidating the lived experiences of participants managing diabetes in a rural context.DiscussionThis study underscores the challenges and strategies of diabetes self-management in rural older adults. A holistic approach, encompassing physical, emotional, and mental well-being, is pivotal, augmented by proactive lifestyle choices. Effective coordination in medication management and enhanced communication among health care providers emerged as essential. The unique role of pets illuminates their profound impact on participants' activity levels and emotional fortitude, suggesting they can be vital assets in diabetes care. Collectively, these findings guide health professionals and policymakers in crafting nuanced, context-sensitive interventions to optimize diabetes management for older adults in rural contexts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.162
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.395
GPT teacher head0.568
Teacher spread0.172 · 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 teacher head, 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

Citations1
Published2025
Admission routes4
Has abstractyes

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