Barriers to nonpharmacologic treatments for stress, anxiety, and insomnia
Bibliographic record
Abstract
OBJECTIVE To explore the attitudes of FPs toward benzodiazepine (BZD) prescribing and the perceived barriers to nonpharmacologic approaches to managing stress, anxiety, and insomnia. DESIGN A questionnaire including 32 statements about treatment of insomnia, stress, and anxiety. SETTING Local quality groups for FPs in Belgium. PARTICIPANTS A total of 948 Belgian FPs. MAIN OUTCOME MEASURES Barriers to using nonpharmacologic approaches in family practice. RESULTS We identified 3 different groups of FPs according to their attitudes about BZD prescribing. A first relatively big group of FPs (39%) were not really concerned about the risks of BZD prescribing. Those in the second group (17%) were aware of the problems associated with BZDs, but did not perceive it to be their role to use nonpharmacologic approaches in family practice. Those in the third group (44%) were concerned about BZD prescribing and found it to be a “bad solution,” but were faced with various barriers to applying nonpharmacologic approaches. Surprisingly, we found that nearly 97% of FPs thought that most people were eligible for nonpharmacologic approaches, but experienced implementation barriers at the level of the patient, the level of the FP, and the level of the health care system. CONCLUSION Using different education and behavioural-change strategies for different FP groups seems important. A large group of FPs does not find prescribing BZDs to be problematic. Sensitizing and alerting FPs to this issue remains very important.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".