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Record W4385728737 · doi:10.1016/j.cjco.2023.08.002

Patient Perspectives on a Tailored Self-Management Education and Support Intervention for Low-Income Seniors With Chronic Health Conditions

2023· article· en· W4385728737 on OpenAlexafffund
Kaitlyn Paltzat, Sara Scott, Kirnvir K. Dhaliwal, Terry Saunders‐Smith, Braden Manns, Tavis S. Campbell, Noah Ivers, Raj Pannu, David J.T. Campbell

Bibliographic record

VenueCJC Open · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryCanadian Institutes of Health ResearchAlberta Innovates
KeywordsSelf-managementIntervention (counseling)Low incomePatient educationGerontologyPsychologyMedicineNursingMedical educationPhysical therapySociologyComputer scienceSocioeconomics

Abstract

fetched live from OpenAlex

Background: tudy (ACCESS) was a 2 x 2 factorial randomized trial that tested the impact of a tailored self-management education support (SMES) program, which demonstrated a 22% reduction in adverse clinical events. We sought to qualitatively explore participants' perspectives on the SMES intervention, and the ways in which it may have improved self-management skills. Methods: We used a qualitative descriptive approach and conducted individual semistructured interviews. We conducted inductive and deductive thematic analysis using NVivo 12 (QSR International, Burlington, MA). Results: We interviewed 20 participants who had recently completed the 3-year SMES intervention. The following 3 main themes emerged from the data: (i) empowerment; (ii) intervention acceptability; and (iii) suggestions for improvement. Regarding empowerment, we identified subthemes of health literacy, self-efficacy, self-management, and active role in health. Several participants reported that empowerment promoted health behaviour change or improved confidence in self-management. Regarding acceptability, we identified subthemes of ease of use and presentation style. Most participants expressed positive feelings toward the intervention and felt that it was easy to understand. Finally, we identified subthemes of learning style, content, and engagement strategies, within the theme of suggestions for improvement. Some participants said that the messages were too general and did not fully address the complex health concerns they had. Conclusions: Our results highlighted key strategies to promote patient engagement and self-management behaviours and demonstrated how they may have been used to improve clinical endpoints. Additionally, we demonstrated the novel use of marketing principles in SMES interventions.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.331
Teacher spread0.320 · 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 designObservational
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

Citations11
Published2023
Admission routes2
Has abstractyes

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