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Record W4401462035 · doi:10.1186/s40900-024-00621-y

Development and evaluation of an anti-ageism advisory group with older adults and gerontological experts: a qualitative descriptive study

2024· article· en· W4401462035 on OpenAlexafffund
Sherry Dahlke, Jeffrey I. Butler, Kelly Baskerville, Mary Fox, Alison L. Chasteen, Kathleen F. Hunter

Bibliographic record

VenueResearch Involvement and Engagement · 2024
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of TorontoYork UniversityUniversity of Alberta
FundersSocial Sciences and Humanities Research Council
KeywordsQualitative researchDescriptive researchGerontologyPsychologyOlder peopleDescriptive statisticsMedical educationMedicineSociologySocial science

Abstract

fetched live from OpenAlex

In recent years, academics have increasingly acknowledged the importance of involving health service users and community stakeholders as active partners in health research. Yet, the involvement of older adults, the largest group of health service users, as research partners remains limited, possibly due to ageist attitudes that devalue older adults’ contributions. During the three years of our Awakening Canadians to Ageism study, we convened an advisory group consisting of older adults and gerontological experts to discuss issues related to ageism, help interpret the study findings, and develop a range of knowledge mobilization strategies to dispel ageism. To understand the experiences of members of the advisory group and solicit recommendations for improving future groups, we conducted a qualitative descriptive study and interviewed 8 older adults and 6 gerontological experts. Data were content analyzed. Four categories that were developed to explain participants’ experiences and suggestions for future advisory groups included: organization and management, group experience, suggestions for future advisory groups and moving forward. A key finding was the value that the older adults and gerontological experts ascribed to conversations about the prevalence of ageism and their desire to continue these types of conversations in their personal groups and professional networks. Numerous helpful strategies for future advisory groups were identified, such as enhancing social diversity, both in terms of racial/ethnic/cultural representation and gender. Older adults wanted more “getting to know you time” in meetings and gerontological experts wanted more details about the research process and their role. This study’s partnership approach can guide researchers seeking to involve key health service users and community stakeholders in health research and help enact positive social change. In 2022 we developed an advisory group consisting of older adults and gerontological experts to review the findings of the first stage of our study Awakening Canadians to Ageism and provide guidance on knowledge mobilization and next steps. We interviewed 12 older adults and 6 gerontological experts from our advisory group to learn about their experiences with the group and provide suggestions for future groups. Participants provided feedback on group organization, management and processes, in addition to their experiences and strategies for future advisory groups. Both groups suggested enhancing the social diversity of the group, both in terms of racial/ethnic/cultural representation and gender. Older adults wanted more meeting time dedicated to getting to know the other groups members and gerontological experts wanted more details about the research process and their role.

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)0.066
metaresearch head score (Gemma)0.064
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 score0.066
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0140.009
Scholarly communication0.0050.005
Open science0.0040.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.392
GPT teacher head0.514
Teacher spread0.122 · 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
Published2024
Admission routes2
Has abstractyes

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