Families and information-sharing in the mental health system
Bibliographic record
Abstract
Family involvement in the mental health system has been identified as evidenced-based best practice in the treatment of people with mental illness. However, family involvement has not been widely embraced by practitioners, and it is suggested that family-blaming may play a role. Confusion around information-sharing policies has been documented as a barrier to interaction between clinicians and families. Therefore, addressing misconceptions around confidentiality may be the gateway to increased family involvement.\n\nThis research was conducted in two parts. Questionnaires and interviews, both taking place relatively independently of the other, were used as assessment tools related to the delivery of an education module. In order to assess if practitioner beliefs and practices around families can be altered by a workshop, questionnaires were completed by participants prior to the module, and five interviews were carried out after delivery of the module. The education module was presented by the researcher to three mental health teams in Vancouver. The module focussed on information-sharing policies, the experiences of families, and the concept of family-blaming. The interview participants were asked for feedback from the module and asked their views on families and family involvement.\n\nThe results had concurrence with the literature review, whereby a connection was identified between beliefs about families and practice with them. Therefore, clinicians who found family involvement beneficial to their clients tended to involve them in their practice, while clinicians who believed that family involvement was not beneficial tended not to involve families in their practice. Confusion exists regarding information-sharing policies, and clinicians are interested in having this clarified. The feedback from the module and an understanding of the beliefs of some practitioners will be helpful in planning future trainings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".