EXPLORING MOBILE CRISIS TEAM TRAINING: A DELPHI STUDY
Bibliographic record
Abstract
The present study is a Delphi design, mixed methods exploration of the training and skills needed for mobile crisis team professionals. It is estimated that 51.5 million adults in the United States live with mental illness (NIMH, 2021). Suicide is the second leading cause of death for those 10-34 years of age (CDC, 2021). When people are in a mental health crisis in the community, they often rely on law enforcement (Lamb et al., 2002) and emergency medical services for aid (Prener & Lincoln, 2015). Although neither profession includes comprehensive mental health training, they are the most common first responders. These interactions do not always end well. The Washington Post (Tan, 2021) reported that between 2016-2021 roughly a quarter of fatal police shootings involved someone in a mental health crisis. Mobile Crisis Team programs have been offered as an alternative to traditional police response. Following community outcry, cities like Baltimore, D.C., and Oakland have worked to create or expand existing mobile crisis programs. During this expansion of mobile crisis services, it is crucial for programs, and the mental health field in general, to have a clear understanding of the types of skills and training needed for mobile crisis professionals. Although there is a large body of research on mobile crisis programs, there is a gap in the literature regarding skills and training. The current study was conducted to address the existing gap in the literature, provide a comprehensive list of skills, training modalities, and professions that compose mobile crisis teams, and inspire future research in mobile crisis training. The study was conducted in three phases. In Phase 1, I recruited a panel of knowledgeable professionals from mobile crisis programs, law enforcement, and emergency medical services to share their expertise on aspects of crisis response in the community. The qualitative data were coded to create a list of skills and training. In Phase 2, panelists rated the items. Descriptive statistics were calculated and included as feedback for Phase 3. In Phase 3, panelists re-rated the items, with feedback, to build consensus. Three lists were produced: (a) Professions Composing Mobile Crisis Teams, (b) Skills and Training, (c) Training Modalities. These lists are composed of 163 items ranked by importance. A high level of consensus regarding importance was achieved by the panel. Differences in item ratings between professions were also explored. The items generated and rated by the panel may prove valuable in the design, improvement, or evaluation of mobile crisis programs and training curricula, and aid in future research on mobile crisis skills, training, design, or effectiveness.
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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 | 0.043 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".