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Record W2974750489 · doi:10.1177/0733464819874600

Fall Prevention Program Characteristics and Experiences of Older Adults and Program Providers in Canada: A Thematic Content Analysis

2019· article· en· W2974750489 on OpenAlexafffundabout
Humna H Malik, Briana Virag, Fiona Fick, Paulette V. Hunter, Sharon Kaasalainen, Vanina Dal Bello‐Haas

Bibliographic record

VenueJournal of Applied Gerontology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of SaskatchewanMcMaster University
FundersSaskatchewan Health Research Foundation
KeywordsThematic analysisFocus groupGerontologyContent analysisPsychologyMedicineNursingQualitative researchBusinessSociology

Abstract

fetched live from OpenAlex

Objectives: To document the characteristics of fall prevention programs in specific regions in two Canadian provinces and to explore older adults’ and program providers’ experiences with these programs. Methods: Semi-structured interviews were conducted with 16 program providers/managers from 12 different programs. Ten semi-structured focus groups were conducted with 59 older adults. Data were analyzed using thematic content analysis. Results: Older adults reported functional and social benefits. Program providers identified barriers to program success, including cognitive impairment, frailty, and lack of motivation. The need for general attitudinal changes toward older adults’ needs and broader community changes were identified as important by the older adults. Discussion: Easily accessible information about fall prevention programs for older adults and no-cost, ongoing initiatives were critical. Health care providers play keys roles in disseminating information, facilitating referrals, and advocating for initiatives that best meet the needs of older adults in their communities.

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)0.000
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 score0.337
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.031
GPT teacher head0.342
Teacher spread0.310 · 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

Citations13
Published2019
Admission routes3
Has abstractyes

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