Bibliographic record
Abstract
Distributed by Bullfrog Films, PO Box 149, Oley, PA 19547; 800-543-FROG (3764)Produced by Catherine MullinsDirected by Catherine Mullins2020, Streaming, 89 mins Saving Minds is a documentary that primarily follows two Montreal, Quebec residents. They are both trying to manage their complex mental health issues, with a new focus on drug reduction and an increase in psychotherapeutic options. Although the film is primarily in English, English subtitles are included for each French-language interview. Even though much of the film is based in Montreal, it also includes physicians, medical journalists, and patients from the United States and England. The film's production is heavily influenced by U.S. medical journalist, Robert Whitaker, who is also featured throughout the film. Author and former mental health patient Joanne Greenberg is also featured for her experience in the traditional mid-20th century U.S. mental health system. Several Canadian, American, and British psychiatrists are interviewed for their perspectives on the evolving transition from a mental health system based on pharmaceutical “cures," to a life-long therapeutic module utilizing drugs as supplemental tools to psychotherapy and peer-based therapeutic models. The film provides a fairly balanced approach to therapeutic options, often reinforcing that mental health therapy should not be one-size-fits-all. It also reinforces that traditional mental health pharmaceutical approaches may lack supporting evidence, while providing basic-level coverage of several current drug reduction therapy studies. Saving Minds should work well in undergraduate and graduate psychology and social work courses, and within coursework for medical, physician's assistant, and nursing students. It also has value as a consumer education resource, especially for those seeking reliable alternatives to traditional drug-focused mental health treatments. The coverage of the subject matter may not be suitable for younger audiences. Awards:Best Human Rights Films, New York Tri-State International Film Festival; Best Educational Documentary, Liber Films International Festival; Best Educational Film, Lion International Film Festival; Best Educational Documentary, Star International Film Festival; Best Educational Documentary, Sofia International Film Festival; Certificate of Achievement, Zagreb International Film Festival
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.630 | 0.245 |
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".