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Record W3011822120 · doi:10.1093/heapro/daaa015

Volunteer engagement to inform research on cardiovascular health awareness, Canada

2020· article· en· W3011822120 on OpenAlexafffundabout
Marie‐Thérèse Lussier, Janusz Kaczorowski, Magali Girard, Emmanuelle Arpin

Bibliographic record

VenueHealth Promotion International · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoCentre Hospitalier de l’Université de MontréalUniversité de MontréalCentre Integre de Sante et de Services Sociaux de Laval
FundersCanadian Institutes of Health Research
KeywordsMedical educationData collectionFocus groupProtocol (science)PhonePsychologyProcess (computing)MedicineNursingAlternative medicine

Abstract

fetched live from OpenAlex

Volunteers have been extensively used in health promotion programmes. However, they have been less frequently involved in the research process. In its most recent iterations, the Cardiovascular Health Awareness Program (CHAP) integrated volunteers (i) to facilitate CHAP sessions with participating patients for data collection and (ii) to evaluate the intervention. Drawing on the patient and public involvement literature, our research team included volunteers in the data collection and evaluation of CHAP sessions as part of the programme's implementation in the province of Quebec (Canada). We sought volunteers' formal feedback through individual online and phone interviews and through focus groups for each of the four projects conducted in Quebec. We found that volunteers provide valuable insight on the research protocol as well as patient needs. Their feedback led to several modifications to the research protocol and procedures of subsequent CHAP sessions. Changes included involving volunteers at earlier stages of the research process, adding more learning modules and practice sessions during the volunteer training and defining research priorities according to patient needs. Our methodology of engaging volunteers in the research process was useful to gain important and unique insight on patient needs and for future programme planning to modify the research process.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.662
GPT teacher head0.554
Teacher spread0.108 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2020
Admission routes3
Has abstractyes

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