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Record W4317613496 · doi:10.1123/japa.2022-0076

Components of a Behavior Change Model Drive Quality of Life in Community-Dwelling Older Persons

2023· article· en· W4317613496 on OpenAlexaff
Nancy E. Mayo, Kedar Mate, Olayinka Akinrolie, Hong Chan, Nancy M. Salbach, Sandra C. Webber, Ruth Barclay

Bibliographic record

VenueJournal of Aging and Physical Activity · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity Health NetworkUniversity of TorontoUniversity of ManitobaMcGill UniversityToronto Rehabilitation InstituteMcGill University Health Centre
Fundersnot available
KeywordsSummative assessmentGerontologyPsychologySample (material)Quality of life (healthcare)Index (typography)Active livingPhysical activityActivities of daily livingMedicineComputer scienceNursingPhysical therapyFormative assessmentWorld Wide WebPedagogy

Abstract

fetched live from OpenAlex

This study aimed to inform a measurement approach for older persons who wish to engage in active living such as participating in a walking program. The Patient Generated Index, an individualized measurement approach, and directed and summative content analyses were carried out. A sample size of 204 participants (mean age 75 years; 62% women) was recruited; it generated 934 text threads mapped to 460 unique categories within 45 domains with similarities and differences for women and men. The Capability, Opportunity, Motivation, and Behaviors Model best linked the domains. The results suggest that older persons identify the need to overcome impaired capacity, low motivation, and barriers to engagement to live actively. These are all areas that active living programs could address. How to measure the outcomes of these programs remains elusive.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.136
GPT teacher head0.374
Teacher spread0.238 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Aging and Physical ActivitySame topicCerebral Palsy and Movement DisordersFrench-language works237,207