Teaching Evidence-Based Medicine and Non-conventional (Alternative) Therapies in Portuguese Veterinary Schools—A Curricular Assessment
Bibliographic record
Abstract
Applying evidence-based veterinary medicine (EBVM) is considered a Day One competence for veterinary graduates. Furthermore, the increasing interest in the use of complementary and alternative (non-conventional) veterinary therapies (NCTs) must be grounded on EBVM principles. Few studies have mapped the teaching of EBVM and of NCTs and assessed their content. This study analyses the official curricula of six (out of eight) Portuguese veterinary schools in terms of EBVM and NCTs, using the self-evaluation documents submitted to the National Agency for Assessment and Accreditation of Higher Education (A3ES) (2014–2015). Results show that, with few exceptions, veterinary education in Portugal follows a traditional, clinically-driven approach to evidence, with concepts taught mostly from an empirical and experiential perspective instead of a systematic one. Core EBVM topics, such as placebo effect, methodological validity, PICO, cognitive bias, and systematic review are either absent or insufficiently covered. Moreover, the teaching of NCTs was found in three out of the six curricular programs, namely acupuncture, phytotherapy, homeopathy, traditional Chinese medicine, aromatherapy, Bach flower remedies, ayurveda, energetic healing (reiki), and massage. We found no evidence that these therapies are being taught under the principles of EBVM. Taken together, these results highlight the need for more explicit and targeted teaching of EBVM-related topics, namely regarding the critical appraisal of scientific literature and the integration of best evidence into clinical decision-making. Results can also be useful to inform the accreditation process by the A3ES and by education quality assurance agencies in other jurisdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".