MétaCan
Menu
Back to cohort
Record W3108331262 · doi:10.1186/s12877-020-01897-x

Attendant’s experience with the personalized citizen assistance for social participation (APIC)

2020· article· en· W3108331262 on OpenAlexafffund
Karine Gagnon, Mélanie Levasseur

Bibliographic record

VenueBMC Geriatrics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsMedicineQualitative researchGerontologyIndependence (probability theory)Independent livingRehabilitationSocial engagementNursingPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: To promote healthy aging, the social participation needs of older adults must be better met. Previous studies have shown the benefits of the Personalized citizen assistance for social participation (APIC), but few explored its influence on attendants. This study explored the assistance experience of attendants in providing the APIC to older adults with disabilities. METHODS: A qualitative design inspired by a phenomenological approach was used with six female attendants who participated in individual interviews. RESULTS: The APIC attendants felt useful, developed meaningful relationships with their older adults, and improved their self-knowledge. Attendants had the opportunity to reflect on their lives and self-aging. They contributed to older adults' functional independence, motivation, and participation in social activities. Attendants encountered challenges related to withdrawn behavior in older adults, such as refusing to participate in activities. CONCLUSIONS: Considering the identified benefits of the APIC for attendants, further studies should explore personalized assistance to preserve older adults' health.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.072
GPT teacher head0.330
Teacher spread0.257 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueBMC GeriatricsSame topicTechnology Use by Older AdultsFrench-language works237,207