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Record W4408473318 · doi:10.2196/66963

Social Participation When Aging With an Early-Onset Neurological Disability: Protocol for Descriptive Qualitative Research

2025· article· en· W4408473318 on OpenAlexaffvenue
Mia Lapointe, Megan Veilleux, Pascale Simard, Hung Manh Nguyen, Angéline Labbé, Valérie Poulin, Samuel Turcotte

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversité du Québec à Trois-RivièresInnovation and Economic Development Trois RivièresUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPreprintQualitative researchProtocol (science)Descriptive researchPsychologyGerontologyMedicineAlternative medicineSociologyWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Due to improvements in health care and rehabilitation, as well as better social conditions, individuals living with traumatic brain injury (TBI), multiple sclerosis (MS), or spinal cord injury (SCI) are living longer. It is therefore necessary to ensure the presence of social and health services adapted to the realities and specific needs of these populations aging with disabilities. Social participation is a key determinant of active aging and health. However, there is limited evidence regarding the social participation of these aging populations. To support the development of more inclusive approaches promoting the health of older adults, it is essential to better understand the diversity of social participation experiences among individuals aging with neurological disabilities. OBJECTIVE: This study aims to explore how social participation is experienced by individuals aging with TBI, MS, or SCI; document the barriers and facilitators to their social participation; and explore avenues for interventions supporting their social participation. METHODS: This descriptive qualitative research is part of a larger action research project conducted in partnership with individuals aging with disabilities, researchers, and community organizations providing services to these populations. Individuals 50 years or older living with TBI (n=8), MS (n=8), or SCI (n=8) will participate in a semistructured interview. The interviews will be transcribed verbatim, and the accuracy of the transcripts will be ensured through peer validation. Qualitative data will be analyzed using a mixed approach in alignment with the Framework methods. The use of the Human Development Model-Disability Creation Process (HDM-DCP) conceptual model will be used for deductive analysis. The coding tree will combine significant themes arising from the interviews' inductive part and the themes from the HDM-DCP. Also, 12.5% of the analysis will be tested for stringency (ie, double-blind and interrater reliability exercise). RESULTS: This study will provide insights into the diversity of social participation experiences of these populations as well as the influence of individual characteristics and environmental resources on their social participation. CONCLUSIONS: This project will lay the groundwork for the codevelopment of health promotion programs aimed at supporting the social participation of individuals aging with neurological disabilities. This study will also help to identify the resources and strengths that support social participation for these populations, as well as the systemic barriers that need to be addressed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66963.

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)0.079
metaresearch head score (Gemma)0.056
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.079
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.056
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0060.007
Science and technology studies0.0110.006
Scholarly communication0.0060.005
Open science0.0060.006
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0500.007

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 head0.576
GPT teacher head0.669
Teacher spread0.093 · 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
GenreProtocol

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

Citations0
Published2025
Admission routes2
Has abstractyes

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