Preparation of this report was commissioned by the Canadian Psychological Association. EXECUTIVE SUMMARY
Bibliographic record
Abstract
• Psychological interventions can effectively treat a wide range of child and adult health problems, including depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, eating disorders, substance abuse, and chronic pain. Furthermore, there is mounting evidence that there are also effective psychological treatments for diseases and disorders that are routinely seen in primary care medical practices but that are typically difficult to medically manage, including type 1 dia-betes, chronic tension-type headaches, rheumatoid arthritis, chronic low-back pain, chronic fatigue syndrome, and a range of medically unexplained physical symptoms. • As emphasized by recent submissions to federal and provincial government depart-ments and commissions by the Canadian Psychological Association, l’Ordre des psy-chologues du Québec, the Manitoba Psychological Society, and the Saskatchewan Psychological Association, psychological services should be an integral component of the Canadian health care system. Not only can psychological interventions be effec-tive in their own right but they have the demonstrated potential to actually reduce health care costs.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.241 | 0.072 |
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".