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Record W2980194240 · doi:10.1093/her/cyz028

What are the factors associated with the implementation of a peer-led health promotion program? Insights from a multiple-case study

2019· article· en· W2980194240 on OpenAlexafffundabout
Agathe Lorthios-Guilledroit, Johanne Filiatrault, Lucie Richard

Bibliographic record

VenueHealth Education Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcGill University Health CentreCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersUniversité de Montréal
KeywordsHealth promotionPromotion (chess)PsychologyMedicineMedical educationNursingPublic healthPolitical science

Abstract

fetched live from OpenAlex

Peer education is widely used as a health promotion strategy. However, few efforts have been undertaken to understand the implementation of peer-led health promotion programs (HPPs). This multiple-case study identifies factors facilitating the implementation of a peer-led HPP for older adults presenting with fear of falling (Vivre en �quilibre) and their mechanisms of action. It used a conceptual framework postulating factors that may influence peer-led HPPs implementation and mechanisms through which such factors may generate implementation outcomes. Six independent-living residences for older adults in Quebec (Canada) implemented Vivre en �quilibre as part of a quasi-experimental study. Implementation factors and outcomes were documented through observation diaries, attendance sheets, peers' logbooks, questionnaires administered to participants and semi-structured interviews conducted among peers, activity coordinators of residences and a subgroup of participants. The analysis revealed three categories of factors facilitating program implementation, related to individuals, to the program and to the organizational context. Three action mechanisms identified in the framework (interaction, self-organization and adaptation) were facilitated by some of these factors. These findings support the application of the peer-led program implementation conceptual framework used in this study and provide insights for practitioners and researchers interested in implementing peer-led HPPs.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.721
GPT teacher head0.734
Teacher spread0.013 · 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 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

Citations4
Published2019
Admission routes3
Has abstractyes

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