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Record W4317878330 · doi:10.1370/afm.21.s1.3816

“It’s Improving Screening Rates, it’s Catching Things Early, and it’s Empowering People”: A Qualitative Study of BETTER WISE

2023· article· en· W4317878330 on OpenAlexaboutno aff
Nicolette Sopcak, Carolina Fernandes, Melanie Wong, Daniel Ofosu, Ielaf Khalil, Mary Ann O’Brien, Tracy K. Y. Wong, Donna Manca

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisContext (archaeology)TeamworkFocus groupPrimary careMedicineNursingQualitative researchIntervention (counseling)Family medicinePsychology

Abstract

fetched live from OpenAlex

Context: BETTER WISE is a comprehensive and structured approach for cancer and chronic disease prevention and screening (CCDPS) that addresses cancer survivorship and screens for lifestyle risks and poverty in patients aged 40 to 65. Objective: To describe the impact, barriers and facilitators of BETTER WISE. Study Design and Analysis: Qualitative study: 17 focus groups and 48 key informant interviews were conducted, transcribed and analyzed employing thematic analysis using a constant comparative method. Written feedback (585 feedback forms) from patients was also collected and analyzed. Setting: 13 primary care settings (urban, rural and remote) in Alberta, Ontario, and Newfoundland and Labrador, Canada. Participants: Primary care team members (N=132) including clinicians, managers and clerical staff participated in focus groups or one-on-one key informant telephone interviews. Additionally, patients submitted 585 written feedback forms. Intervention: 1,005 patients were invited for a one-hour visit with a “prevention practitioner” (PP), a member of the primary care team with training in CCDPS and the BETTER WISE approach. PPs met with patients one-on-one to provide them with an overview of their individual risk for chronic diseases, eligibility for screening, and assistance with lifestyle counselling. Results: Four main themes were identified: 1) Impact on patients: Patients appreciated the BETTER WISE approach and found it empowering. They caught health concerns that were overlooked and reported improved lifestyle changes; 2) Impact on primary care providers (PCPs): PPs reported improved teamwork, increased knowledge of CCDPS, and better relationships with patients and physicians; 3) The main barrier to implementation of BETTER WISE was the onset of the COVID-19 pandemic, which: i) changed prevention visits to phone visits, ii) put screening tests on hold, and iii) added hardship on patients and PCPs as focus shifted to emergencies, acute care, and COVID-19 screening and vaccinations; 4) Facilitators of the implementation of BETTER WISE included: i) buy-in from PPs, physicians, and patients, ii) good relationship and team culture within primary care teams, and iii) alignment with CCDPS activities already happening at the clinics. Conclusion: Despite the interruption of the COVID-19 pandemic, the participating primary care clinics completed the BETTER WISE study and the BETTER WISE approach had a positive impact on patients and PCPs.

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)0.022
metaresearch head score (Gemma)0.024
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 score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.003
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 head0.037
GPT teacher head0.359
Teacher spread0.322 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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