Understanding determinants of patients’ decisions to attend their family physician and to take antibiotics for upper respiratory tract infections: a qualitative descriptive study
Bibliographic record
Abstract
Abstract Background Although antibiotics have little or no benefit for most upper respiratory tract infections (URTIs), they continue to be prescribed frequently in primary care. Physicians perceive that patients’ expectations influence their antibiotic prescribing practice; however, not all patients seek antibiotic treatment despite having similar symptoms. In this study, we explored patients’ views about URTIs, and the ways patients manage them (including attendance in primary care and taking antibiotics). Methods Using a qualitative descriptive design, adult English-speaking individuals at a Canadian health center were recruited through convenient sampling. The participants were interviewed using semi-structured interview guide based on the Common Sense-Self-Regulation Model (CS-SRM). The interviews were transcribed verbatim and coded according to CS-SRM dimensions (illness representations, coping strategies). Sampling continued until thematic saturation was achieved. Thematic analysis related to the dimensions of CS-SRM was applied. Results Generally, participants had accurate perception about the symptoms of URTIs, as well as how to prevent and manage them. However, some participants revealed misconceptions about the causes of URTIs. Almost all participants mentioned that they only visited their doctor if their symptoms got progressively worse and they could no longer self-manage the symptoms. When visiting a doctor, most participants reported that they did not seek antibiotics. They expected to receive an examination and an explanation for their symptoms. Conclusion Our participants reported good understanding regarding the likely lack of benefit from antibiotics for URTIs. Developing interventions that specifically help patients discuss their concerns with their physicians, instead of providing more education to public may help in reducing the use of unnecessary antibiotics.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".