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Record W4285301554 · doi:10.1177/20543581221100292

The Renal Community Photo Initiative: A Program Report in Ontario, Canada

2022· article· en· W4285301554 on OpenAlexafffundabout
Ruth Skinner, Cindy House, Andrew A. House, Chris McIntyre, Elaine Hayter, Pamela Ireland, Jared McGregor, Ann Tillmann

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsLondon Health Sciences CentreWestern University
FundersWestern University
KeywordsPhotovoiceParticipatory action researchFocus groupAgency (philosophy)MedicineMedical educationQualitative researchParticipant observationCitizen journalismThematic analysisPublic relationsNursingSociology

Abstract

fetched live from OpenAlex

Purpose of Program: We initiated the Renal Community Photo Initiative to better understand why some hemodialysis users express significant capacities for resilience and hope, demonstrating outward-looking perspectives and enjoying a rich quality of life. Sources of Information: "Photovoice" is a participatory research methodology that encourages individuals to develop positive self-perceptions with photography. Photovoice empowers participants as informants within their communities. Visual arts researchers surveyed existing Photovoice studies to identify gaps in knowledge to be addressed in this study, including challenges related to fostering participant agency and social action. Images and logs were collected and reviewed during organized, participant-led substudy groups. These meetings provided researchers with core study values and direction as to how the images and additional information should be used to raise awareness about living with chronic kidney disease. Methods: To address the complexity of the human condition, the Renal Community Photo Initiative offered participants an array of diverse and accessible image-making techniques. No narrative directives for image-making were provided. This qualitative, interdisciplinary, participant-centric study invited adult chronic hemodialysis patients in 4 dialysis units in London and Stratford, Ontario, to participate. The research team designed a selection of different, accessible photo technologies for participant use. Eligible participants were invited to select photographic technologies and given the additional option to write accompanying logs. Researchers organized substudy meetings for participant-led focus groups to discuss core study values and personal encounters with images and image-making. Participants directed how their generated images should be shared with the public and researchers. Key Findings: A total of 40 participants have been recruited to date, producing more than 1600 images and an archive of handwritten logs. Three participant-led focus groups have established priorities for image sharing and a core set of values for subsequent study phases. A series of public presentations of participant images took place. The research team will pursue further public presentation opportunities and the development of a suitable research database. Limitations: Organizing and categorizing images for access in an interdisciplinary research database remains a challenge. Current health and safety protocols related to COVID-19 require the study to pause recruitment and substudy meetings and reassess immediate outputs for visuals. Implications: A qualitative study of this scope offers a new model for participant agency and collaboration. It requires the onboarding of interdisciplinary researchers to effectively engage with its significant image and log archive. Participants should remain involved in directing future steps for disseminating their images. Following substudy directives, researchers are developing visuals for health care and public settings, and determining opportunities for participants to share their experience in both clinic- and public-based settings.

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)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.372
GPT teacher head0.548
Teacher spread0.175 · 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.

Study designNot applicable
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
Published2022
Admission routes3
Has abstractyes

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