Alternative crisis mental health responses: Protocol for a knowledge-to-action study
Bibliographic record
Abstract
Alternative crisis mental health responses are models that differ from the default, policy-only model of responding to emergency reports of acute psychological distress in the community. The two prevalent alternative models are co-response, pairing police with a civilian (such as a nurse or social worker) and civilian response, whereby first responders are not police. Two scoping reviews have been completed that synthesize key processes from the literature that underpin co-response and civilian response. Informed by transformative justice and the principle of the collective, this protocol details a knowledge-to-action (KTA) study whereby summaries of those scoping reviews will be used as prompts for semi-structured interviews with interest-holders (frontline service providers, people with lived experience of mental health crises, policymakers, and mental health scholars and advocates) across the United States and Canada. Interviews will focus on participants’ experiences with and perceptions of crisis response, as well as their opinions about the accuracy and relevance of the scoping reviews. The analytic plan incorporates content analysis and interpretive phenomenology to capture both the patterns that emerge and the meanings that participants attach to their experiences, producing a more layered and comprehensive analysis than either method could achieve alone. This protocol represents one of the first research plans to apply the KTA cycle and the first known study to propose that a team of researchers first synthesize the literature and then interview key informants about the accuracy and applicability of those knowledge syntheses. Findings may highlight discrepancies between the literature and the field, with a focus on alternative crisis mental health response.
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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 | 0.168 | 0.172 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.167 | 0.047 |
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".