11 Enablers and barriers related to quaternary prevention from a range of perspectives: a descriptive study using semi-structured interviews
Bibliographic record
Abstract
Background With global concern about sustainability in health care, there is growing interest in the impacts of ‘overuse’ on patients and systems and calls for addressing this problem. The concept of quaternary prevention (P4), a strategy to protect patients from medical interventions in which harms outweigh benefits, is well-positioned to provide 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. Methods We conducted a qualitative study using Grounded Theory approach. Field experts in British Columbia, Canada were recruited based on their interest and work related to P4 and related concepts using purposive and theoretical sampling. Qualitative in-depth interviews (n = 12) with a semi-structured interview guide were conducted virtually and transcribed. Inductive and deductive coding approaches were used for analysis, and codes were grouped into themes. We created a conceptual model to visually demonstrate relationships between barriers and enablers of P4. Results 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 & screening campaigns, cognitive biases, cultural factors, complexity of the problem, and industry influence. 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. Conclusions Elicited facilitators and impediments to 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. It is the complex interplay between factors that determines whether a patient receives care that is appropriate to their needs and goals, and whether the planning of that care incorporates the intention of minimizing the harms of tests and treatments. A transition to a model of care that explicitly integrates conscious protection of patients will require increased evaluation of potential solutions and 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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".