A personalized biomedical risk assessment infographic for people who smoke with COPD: a qualitative study
Bibliographic record
Abstract
Abstract Background Chronic obstructive pulmonary disease (COPD) causes 3 million deaths each year, yet 38% of COPD patients continue to smoke. Despite proof of effectiveness and universal guideline recommendations, smoking cessation interventions are underused in practice. We sought to develop an infographic featuring personalized biomedical risk assessment through future lung function decline prediction (with vs without ongoing smoking) to both prompt and enhance clinician delivery of smoking cessation advice and pharmacotherapy, and augment patient motivation to quit. Methods We recruited patients with COPD and pulmonologists from a quaternary care center in Toronto, Canada. Infographic prototype content and design was based on best evidence. After face validation, the prototype was optimized through rapid-cycle design. Each cycle consisted of: (1) infographic testing in a moderated focus group and a clinician interview (recorded/transcribed) (with questionnaire completion); (2) review of transcripts for emergent/critical findings; and (3) infographic modifications to address findings (until no new critical findings emerged). We performed iterative transcript analysis after each cycle and a summative qualitative transcript analysis with quantitative (descriptive) questionnaire analysis. Results Stopping criteria were met after 4 cycles, involving 20 patients (58% male) and 4 pulmonologists (50% male). The following qualitative themes emerged: Tool content (infographic content preferences); Tool Design (infographic design preferences); Advantages of Infographic Messaging (benefits of an infographic over other approaches); Impact of Tool on Determinants of Smoking Cessation Advice Delivery (impact on barriers and enablers to delivery of smoking cessation advice in practice); and Barriers and Enablers to Quitting (impact on barriers and enablers to quitting). Patient Likert scale ratings of infographic content and format/usability were highly positive, with improvements in scores for 20/21 questions through the design process. Providers scored the infographic at 77.8% (“superior”) on the Suitability Assessment of Materials questionnaire. Conclusions We developed a user preference-based personalized biomedical risk assessment infographic to drive smoking cessation in patients with COPD. Our findings suggest that this tool could impact behavioural determinants of provider smoking-cessation advice delivery, while increasing patient quit motivation. Impacts of the tool on provider care, patient motivation to quit, and smoking cessation success should now be evaluated in real-world settings.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".