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Record W4385837495 · doi:10.3389/fresc.2023.1064206

What matters to program partners when implementing a community-based exercise program for people post-stroke? A theory-based qualitative study and cost analysis

2023· article· en· W4385837495 on OpenAlexafffund
Gayatri Aravind, Kainat Bashir, Jill I. Cameron, Mark Bayley, Robert Teasell, Jo-Anne Howe, Alda Tee, Susan Jaglal, Susan Hunter, Nancy M. Salbach

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsRoyal Victoria Regional Health CentreParkwood InstituteWestern UniversityUniversity of TorontoUniversity Health NetworkMarch of Dimes CanadaToronto Rehabilitation Institute
FundersUniversity of TorontoToronto Rehabilitation InstituteHeart and Stroke Foundation of Canada
KeywordsRecreationGeneral partnershipFocus groupHealth careQualitative researchPsychologyMedical educationNursingApplied psychologyMedicineBusinessMarketingSociologyPolitical science

Abstract

fetched live from OpenAlex

Background: Community-based exercise programs integrating a healthcare-community partnership (CBEP-HCP) can facilitate lifelong exercise participation for people post-stroke. Understanding the process of implementation from multiple perspectives can inform strategies to promote program sustainability. Purpose: To explore stakeholders' experiences with undertaking first-time implementation of a group, task-oriented CBEP-HCP for people post-stroke and describe associated personnel and travel costs. Methods: We conducted a descriptive qualitative study within a pilot randomized controlled trial. In three cities, trained fitness instructors delivered a 12-week CBEP-HCP targeting balance and mobility limitations to people post-stroke at a recreation centre with support from a healthcare partner. Healthcare and recreation managers and personnel at each site participated in semi-structured interviews or focus groups by telephone post-intervention. Interviews and data analysis were guided by the Consolidated Framework of Implementation Research and Theoretical Domains Framework, for managers and program providers, respectively. We estimated personnel and travel costs associated with implementing the program. Results: Twenty individuals from three sites (4 recreation and 3 healthcare managers, 7 fitness instructors, 3 healthcare partners, and 3 volunteers) participated. We identified two themes related to the decision to partner and implement the program: (1) Program quality and packaging, and cost-benefit comparisons influenced managers' decisions to partner and implement the CBEP-HCP, and (2) Previous experiences and beliefs about program benefits influenced staff decisions to become instructors. We identified two additional themes related to experiences with training and program delivery: (1) Program staff with previous experience and training faced initial role-based challenges that resolved with program delivery, and (2) Organizational capacity to manage program resource requirements influenced managers' decisions to continue the program. Participants identified recommendations related to partnership formation, staff/volunteer selection, training, and delivery of program activities. Costs (in CAD) for first-time program implementation were: healthcare partner ($680); fitness coordinators and instructors ($3,153); and participant transportation (personal vehicle: $283; public transit: $110). Conclusion: ClinicalTrials.gov, NCT03122626. Registered April 17, 2017-Retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT03122626.

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)0.020
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.035
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.004
Scholarly communication0.0040.005
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.429
Teacher spread0.387 · 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 designQualitative
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

Citations7
Published2023
Admission routes2
Has abstractyes

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