Enablers and barriers to a quaternary prevention approach: a qualitative study of field experts
Bibliographic record
Abstract
OBJECTIVE: There is a growing concern about the sustainability of healthcare and the impacts of 'overuse' on patients and systems. Quaternary prevention (P4), a concept promoting the protection of patients from medical interventions in which harms outweigh benefits, is well positioned to stimulate reflection and inspire solutions, yet has not been widely adopted. We sought to identify enablers and barriers to a P4 approach, according to field experts and advocates in one health system. DESIGN: Qualitative methodology, using semistructured interviews and a grounded theory approach facilitated thematic analysis and development of a conceptual model. SETTING: Virtual interviews, conducted in British Columbia, Canada. PARTICIPANTS: 12 field experts, recruited based on their interest and work related to P4 and related concepts. RESULTS: Four factors were seen as promoting or hindering P4 efforts depending on context: relationship between patient and clinician, education of clinicians and the public, health system design and influencers. We extracted four broad enablers of P4: evidence-based medicine, personal experiences and questioning attitude, public P4 campaigns and experience in resource-poor contexts. There were six barriers: peer pressure between clinicians, awareness and screening campaigns, cognitive biases, cultural factors, complexity of the problem and industry influence. CONCLUSIONS: Elicited facilitators and impediments to the application of P4 were similar to those seen in existing literature but framed uniquely; our findings place increased emphasis on the clinician-patient relationship as central to decision-making and position other drivers as influencing this relationship. A transition to a model of care that explicitly integrates conscious protection of patients by reducing overtesting, overdiagnosis and overtreatment will require changes across health systems and society.
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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 | 0.041 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".