[Preliminary results of a focus-group-based research project on the problems of implementing evidence-based practice in Belgium (Flanders). Do psychiatrists differ from other health care practitioners?].
Bibliographic record
Abstract
BACKGROUND: The impact of evidence-based practice (EBP) has increased substantially in recent years. However, health care practitioners are experiencing difficulties in implementing EBP. AIM: The specific task was to find out what problems are encountered by Flemish (Belgian, Dutch-speaking) health care practitioners. method In order to explore this problem, we adopted a qualitative research strategy and set up 25 focus groups, 5 of which consisted solely of psychiatrists. results Psychiatrists shared with other health care disciplines some concerns about the characteristics of 'evidence' and about the influential role played by their 'partners' in the health care system, namely by government, commercial firms and patients. Psychiatrists perceived their discipline to be much more complex than other disciplines, particularly in areas such as research design, patients' problems, psychiatric diagnosis and therapeutic psychiatrist-patient relationships. The literature and the preliminary results of ongoing research revealed that other disciplines too are confronted with similar complexities. CONCLUSION: There seems to be no justification for ruling out the possibility of implementing EBP on the basis of discipline-related barriers.
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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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".