The impact of personal pseudoscientific beliefs in the pursuit for non-evidence-based health care
Bibliographic record
Abstract
INTRODUCTION: Pseudoscientific beliefs are widespread in society and are influenced by several factors. The endorsement of alternative medicine treatments, mostly not evidence based, has relevant negative impacts on health care public policies. The understanding of the impact of pseudoscientific beliefs on the endorsement of alternative treatments is a relevant issue in this matter. OBJECTIVES: We aim at describing scientific and pseudoscientific beliefs and its impact on the endorsement of evidence and non-evidence-based health care treatments. METHOD: We conducted a survey in a representative sample of 2,091 participants from all Brazil geopolitical regions and 130 different cities. We measured knowledge about health treatments, including alternative medicine treatments, and trust in each treatment, if treatment had been previously sought, if treatments should be funded by the public health system, among other issues. We also measured beliefs in scientific and pseudoscientific claims using a 5-point Likert agreement scale with 9 items with two factors: Scientific beliefs and Pseudoscientific beliefs. RESULTS: Our results show that most part of the sample recognizes conventional medicine as a treatment (64.5%), but also alternative medicine practices such as homeopathy (69.2%), and spiritual therapy (68.6%). We found that support of all alternative medicine treatments is significantly predicted by pseudoscientific beliefs (betas regression coefficients ranging from .13 to .38 all p <.01). On the other hand, the support of evidence-based medicine is rooted in scientific beliefs (beta = .12, p<.01). CONCLUSION: Our results have shown a high rate of prevalence of pseudoscientific beliefs related to non-evidence-based health treatments. It also shreds a favorable evidence that general pseudoscientific beliefs are relevant to assess the endorsement of non-evidence-based healthcare.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".