Evidence‐informed person‐centered healthcare part I: Do ‘cognitive biases plus’ at organizational levels influence quality of evidence?
Bibliographic record
Abstract
INTRODUCTION: There is increasing concern about the unreliability of much of health care evidence, especially in its application to individuals. HYPOTHESIS: Cognitive biases, financial and non-financial conflicts of interest, and ethical violations (which, together with fallacies, we collectively refer to as 'cognitive biases plus') at the levels of individuals and organizations involved in health care undermine the evidence that informs person-centred care. METHODS: This study used qualitative review of the pertinent literature from basic, medical and social sciences, ethics, philosophy, law etc. RESULTS: Financial conflicts of interest (primarily industry related) have become systemic in several organizations that influence health care evidence. There is also plausible evidence for non-financial conflicts of interest, especially in academic organizations. Financial and non-financial conflicts of interest frequently result in self-serving bias. Self-serving bias can lead to self-deception and rationalization of actions that entrench self-serving behaviour, both potentially resulting in unethical acts. Individuals and organizations are also susceptible to other cognitive biases. Qualitative evidence suggests that 'cognitive biases plus' can erode the quality of evidence. CONCLUSIONS: 'Cognitive biases plus' are hard wired, primarily at the unconscious level, and the resulting behaviours are not easily corrected. Social behavioural researchers advocate multi-pronged measures in similar situations: (i) abolish incentives that spawn self-serving bias; (ii) enforce severe deterrents for breaches of conduct; (iii) value integrity; (iv) strengthen self-awareness; and (v) design curricula especially at the trainee level to promote awareness of consequences to society. Virtuous professionals and organizations are essential to fulfil the vision for high-quality individualized health care globally.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.390 | 0.712 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".