Revisiting Balint's innovation: enhancing capacity in collaborative mental health care
Bibliographic record
Abstract
Interprofessional collaboration is increasingly recognized as a key response to the challenges associated with complex mental health issues in community primary-care settings. Relatively few practice models, however, provide an orientation and a structure that combines quality patient care, professional development, and the building of community capacity. A psychodynamic tradition of supervision and collaboration, an approach known as the Balint model, holds considerable potential to bring this orientation to collaborative primary care and mental health teams. As a consultation group, the Balint approach brings participants' attention to subtle emotional-interpersonal phenomena such as the provider-patient relationship, the presentation of illness, and the experiences of patients and team members. We introduce and provide an overview of the Balint group model, including several concepts proposed by Balint to illuminate the emotional and relational complexities of providing mental health care in a collaborative primary-care setting. The context of our discussion is the implementation of a modified Balint group approach within a Canadian collaborative mental health Care (CMHC) program. We also discuss how an interprofessional application of this approach can enhance patient care, contribute to care providers' professional development, and build community capacity.
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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 | 0.050 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".