The story under the story: narrative therapy with individuals in\na relationship with psychosis
Bibliographic record
Abstract
The current treatment for the phenomena of psychosis is predominantly bio-medical in spite of other documented causes such as abuse, trauma and substance use. This type of approach is too narrow and fails to be inclusive of social, relational and socio-economic domains of reality. There has been recent momentum within the Mental Health Commission of Canada, and at various macro levels, to include approaches that are collaborative, respectful and supportive of a person-centered path to wellness and wellbeing. Based on a social constructivist approach rooted in a critical psychiatry perspective this study explored the observations and experiences of five service providers, counsellors and social workers, in regard to how narrative therapy contributes to well-being for individuals who have a story that includes the phenomena identified as psychosis. Their experiences were captured through qualitative semi-structured interviews. The themes that emerged through the interviews were impacts and dangers, personal agency, nuanced meanings and narrative therapy as a conduit. These themes identify the work that is being done in this area and the viewpoints of social workers and counselors who utilize a social constructivist lens. These themes also provide direction as to the emerging practice of narrative therapy in this area of interest. Implications for social work practice, policy and research are discussed which provide a promising future for narrative therapy and working with the phenomena of psychosis.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".