EXPLORING MOBILE CRISIS TEAM TRAINING: A DELPHI STUDY
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».