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Record W2945836486 · doi:10.1177/0733464819850083

Assessment of Implementation Outcomes of a Peer-Led Program Targeting Fear of Falling Among Older Adults

2019· article· en· W2945836486 on OpenAlexafffundabout
Agathe Lorthios-Guilledroit, Johanne Filiatrault, Lucie Richard

Bibliographic record

VenueJournal of Applied Gerontology · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersFonds de Recherche du Québec-Société et CultureUniversité de MontréalFonds de Recherche du Québec - SantéPublic Health Agency of Canada
KeywordsFear of fallingAttendancePhonePeer mentoringGerontologyFidelityFalling (accident)MedicinePeer groupQualitative researchPsychologyMedical educationPoison controlSuicide preventionEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

Background: This study examined the implementation outcomes (program reach, fidelity, adaptations, responsiveness) of a peer-led program for older adults with fear of falling — Vivre en Équilibre (VEE). Method: VEE was implemented in six independent-living residences for older adults in Quebec (Canada) as part of an effectiveness study. Implementation outcomes were documented using attendance sheets, peer leaders’ logbooks, observation sheets, and phone-administered questionnaires. Qualitative interviews were also conducted with peer leaders, activity coordinators of residences, and a subsample of program participants. Results: The program reached 71 participants who generally corresponded to the program’s target population. Peer leaders delivered the program with moderate to high fidelity but adapted some elements. Responsiveness was good, as reflected by a high attendance rate (91%) and respondents’ satisfaction levels. Conclusion: Findings revealed that VEE was well implemented, suggesting that it can be successfully delivered by peer leaders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.356
Teacher spread0.344 · 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 designObservational
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

Citations10
Published2019
Admission routes3
Has abstractyes

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