A Canadian Primary Care Sentinel Surveillance Network Study Evaluating Antidepressant Prescribing in Canada from 2006 to 2012
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prescribing patterns of antidepressants (ADs) by primary care providers to youth, adults, and seniors, from 2006 to 2012, using data from electronic medical records (EMRs). METHOD: This was a retrospective cross-sectional database study that used primary care data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Data on more than 600 000 Canadian primary care patients were used to determine the prevalence and incidence of AD prescribing to patients 15 years and older who had an encounter in the years of study (from 2006 to 2012). Each study year was evaluated independently. RESULTS: The study population consisted of 86 927 patients in 2006 (mean age 48.1 years [SD 18.7], 38% male) and grew to 273 529 (mean age 49.6 years [SD 19.3], 40% male) in 2012. The prevalence of AD prescribing increased from 9.20% in 2006 to 12.80% in 2012 (P < 0.001). While the incidence rate of AD prescribing dropped from 3.54% in 2006 to 2.72% in 2008 (P < 0.001) the rate started to significantly rise again, reaching an incidence of 3.07% by 2012 (P < 0.001). CONCLUSIONS: The prevalence of AD prescribing by primary care providers in Canada continued to rise from 2006 to 2012. Conversely, incidence has remained stable or declined during the 6-year study period. While many complex factors likely contribute to the observed prevalence and incidence rates, our findings suggest that the guidelines indicating the efficacy of long-term AD therapy for patients with highly recurrent or severe depression are being followed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".