Prescription of benzodiazepines and antidepressants among Sami and non-Sami — How childhood violence shapes prescription patterns: the SAMINOR 2 questionnaire survey and the Norwegian prescription database
Bibliographic record
Abstract
Abstract Background Medication for mental health problems represents a significant proportion of overall medication use and the prescription of psychotropic medicine has increased in many western countries over the last decades. Childhood violence (CV) is strongly associated with mental health problems, which in turn may increase the likelihood of being prescribed psychotropic medication. However, the association between CV and prescription of benzodiazepines (BDZ) and antidepressants is rarely described, and no such study has been performed among the Indigenous Sami people. Methods Data from the SAMINOR 2 Questionnaire Survey (2012) was linked to the Norwegian Prescription Database. Information on filled prescriptions for BDZ and antidepressants in 2004–2019 was collected for 11,296 persons (55.8% women, 22.6% Sami). Gender-stratified chi-square tests and two-sample t-tests were used to test for differences between groups. Logistic regression was applied to investigate the association between CV and filled prescriptions for BDZ and antidepressants. Results During the 16-year study period, 16.7% of all women filled at least one prescription for BDZ. The figures were significantly lower among Sami women (14.1%) compared to non-Sami women (17.4%) (p = .003). Among all women, 23.6% filled at least one prescription for antidepressants, with no difference between ethnic groups. Filled prescriptions among men were 10.0% and 14.2%, respectively, with no difference between ethnic groups. During each year, and in total, a significantly higher proportion of women exposed to CV received at least one prescription for BDZ and antidepressants, respectively, compared to women not exposed to CV, with no differences between ethnic groups. Among men, the pattern was similar. Conclusion A lower proportion of Sami women filled prescriptions for BDZ than non-Sami women. Those who reported exposure to CV filled prescriptions for BDZ and antidepressants more often than those who did not report CV. There were no overall differences between Sami and non-Sami; the dispensing rates of antidepressants and BDZ were similar for Sami and non-Sami, and the effects of CV on the dispensing of antidepressants and BDZ were also similar. This study highlights the importance of preventing CV, and of identifying a history of CV when treating adults with mental health problems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".