Models of practice and training in psychotherapy: cross-national perspectives from Italy and Canada
Bibliographic record
Abstract
Internationally, there is ongoing concern about accessibility to mental health care and training. The goal of this study was to explore commonalities and differences within models of clinical psychology and psychotherapy in Ontario, Canada, and Lombardia, Italy, respectively, to inform improvements to the accessibility of mental health care and training. Using key informant sampling, we recruited ten students and professionals in Italy and Canada who study or work in psychology for semi-structured interviews. We analyzed the interview content using an inductive approach for thematic analysis within countries and meta-theme analysis across countries. The findings indicated three cross-national meta-themes: the need to integrate evidence with practice, the limited accessibility of training for students and treatment for patients, and the importance of the quality of training programs. Despite some differences regarding the amount of scientific training, personal therapy for trainees, and the prominence of cultural diversity training, Canadian and Italian psychology professionals and students shared experiences of psychotherapy practice and clinical psychology training. The three cross-national meta-themes indicate which issues in training and practice may be relevant worldwide and where to focus resources. The findings can inform international collaborations regarding training model structures that may increase access to psychology training and may increase consensus on professional recognition standards to improve mobility for professionals. These changes could reduce barriers to mental healthcare services for patients.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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; 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".