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Record W3016866318 · doi:10.1155/2020/6215428

Participants’ Perspectives of a Primary Exercise-Based Prevention Program for Cardiac Patients: A Prepost Intervention Qualitative Case Study

2020· article· en· W3016866318 on OpenAlexaff
Mélissa Lesage-Moussavou-Nzamba, Julie Houle, François Trudeau

Bibliographic record

VenueRehabilitation Research and Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMedicineCoping (psychology)Intervention (counseling)Qualitative researchPerceptionPrimary preventionDiseaseFamily medicinePhysical therapyNursingClinical psychologyPsychology

Abstract

fetched live from OpenAlex

Perseverance in exercise-based, cardiovascular disease prevention programs is generally very low. The purpose of this case study is to understand the experience of participants enrolled in a 6-month primary and secondary exercise-focused, cardiovascular disease prevention out of hospital program. Ten participants were interviewed about their experiences at entry and after it ended 6 months later to understand the facilitators and difficulties encountered by participants in such exercise programs. Four out of ten participants completed the 6-month program. The six participants who left the program accepted to contribute to the postprogram interview. The results showed that the four participants who persevered in the program became aware of cardiac risk factors and their conditions were willing to make changes in their lifestyles to reach their objectives, felt a strong perception of self-efficacy, and felt like they belonged in the program. Both persevering and nonpersevering participants experienced many episodes of discouragement during the program and faced many barriers that interfered with their progress. Suggestions to help coping with these barriers while reinforcing self-efficacy and the sentiment of belonging are discussed.

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)0.007
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.201
GPT teacher head0.572
Teacher spread0.371 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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