Assessing the methodological quality of the Canadian Psychiatric Association's anxiety and depression clinical practice guidelines
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Clinical practice guidelines (CPGs) endeavour to incorporate the best available research evidence together with the clinically informed opinions of leading experts in order to guide clinical practice when dealing with a given condition. There has been increased interest in CPGs that are evidence based and that promote best practice, a central component of which is incorporating the best available research predicated on strong study designs. Despite this soaring interest, there remains heterogeneity in the methodological quality of many CPGs, which may have an effect on the quality of services that clinicians offer. In light of this, this study examined the quality of the methodology used to develop two CPGs of the Canadian Psychiatric Association (CPA). METHOD: The CPA's guidelines for the management of anxiety disorders (2006) and for the treatment of depressive disorders (2001) were assessed by trained raters using the Appraisal of Guidelines for Research and Evaluation II Instrument scale. RESULTS: The blind ratings of three trained raters demonstrated that the anxiety and depression CPGs had a number of strengths and important weaknesses. CONCLUSION: Implications for the development of future CPGs on anxiety and depression, including recommendations to improve guideline quality in psychiatry in particular, are discussed.
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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 | 0.673 | 0.871 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".