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Record W2914138940 · doi:10.1017/jsc.2018.13

‘When you put the Group and the Running Together. . .’: A Qualitative Examination of Participant Experiences of the Canadian Run to Quit program

2018· article· en· W2914138940 on OpenAlexafffundabout
Krista Glowacki, Meghan O’Neill, Carly S. Priebe, Guy Faulkner

Bibliographic record

VenueThe Journal of Smoking Cessation · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersCanadian Institutes of Health ResearchInstitut pour la Recherche en Santé PubliquePublic Health Agency of Canada
KeywordsSmoking cessationThematic analysisPsychologyApplied psychologyPsychological interventionPhoneMedical educationHelplineQualitative researchMedicinePsychiatry

Abstract

fetched live from OpenAlex

Introduction: Run to Quit is a national community-based program that combines smoking cessation support with physical activity through learn to run group-based curriculum, self-help and smoking cessation materials. The program is currently in a three-year scaling up phase. Aims: The aim of the current study is to explore participant experiences of the Run to Quit program after its first year, and identify potential areas of improvement for future iterations of the program. Methods: Participants (n = 55) were interviewed over the phone at the end of the 10-week program. Participant interviews were recorded and transcribed. A thematic analysis was conducted. Results/Findings: Participants were satisfied with the program. Strengths of the program were the group aspect, supervised participation and the running. Weaknesses were seen as the variability in walking and running abilities and inadequate engagement by the Smokers Helpline. Many people who successfully quit smoking reported using additional quit aids. Non-completers of the program gave mostly logistical and personal reasons for dropout. Conclusions: Overall, Run to Quit was well received by participants. Multiple health behaviour interventions at a scalable level appear feasible. Based on participant feedback, key recommendations to improve the program in the future include greater tailoring to walking or running preference, and increasing engagement with the Smokers Helpline.

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)0.006
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.083
GPT teacher head0.367
Teacher spread0.285 · 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 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

Citations5
Published2018
Admission routes3
Has abstractyes

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