Priority Given to Technology in Government-Based Mental Health and Addictions Vision and Strategy Documents: Systematic Policy Review (Preprint)
Notice bibliographique
Résumé
BACKGROUND The use of information and communication technologies (ICTs) to deliver mental health and addictions (MHA) services is a global priority, especially considering the urgent shift towards virtual delivery of care in response to the COVID-19 pandemic. It is important to monitor the evolving role of technology in MHA services. Given that MHA policy documents represent the highest level of priorities for a government’s vision and strategy for mental health care, one starting point is to measure the frequency with which technology is mentioned and the terms used to describe its use in MHA policy documents (before, during, and after COVID-19). Yet, to our knowledge, no such review of the extent to which ICTs are referred to in Canadian MHA policy documents exists to date. OBJECTIVE The objective of this systematic policy review was to examine the extent to which technology is addressed in Canadian government–based MHA policy documents prior to the COVID-19 pandemic to establish a baseline for documenting change. METHODS We reviewed 22 government-based MHA policy documents, published between 2011 and 2019 by 13 Canadian provinces and territories. We conducted content analysis to synthesize the policy priorities addressed in these documents into key themes, and then systematically searched for and tabulated the use of 39 technology-related keywords (in English and French) to describe and compare jurisdictions. RESULTS Technology was addressed in every document, however, to a varying degree. Of the 39 searched keywords, we identified 22 categories of keywords pertaining to the use of technology to deliver MHA services and information. The 6 most common categories were tele (n=16/22), phone (n=12/22), tech (n=11/22), online (n=10/22), line (n=10/22), and web (n=10/22), with n being the number of policy documents in which the category was mentioned out of 22 documents. The use of terms referring to advanced technologies, such as virtual (n=6/22) and app (n= 4/22), were less frequent. Additionally, policy documents from some provinces and territories (eg, Alberta and Newfoundland and Labrador) mentioned a diverse range of ICTs, whereas others described only 1 form of ICT. CONCLUSIONS This review indicates that technology has been given limited strategic attention in Canadian MHA policy. Policy makers may have limited knowledge on the evidence and potential of using technology in this field, highlighting the value for knowledge translation and collaborative initiatives among policy makers and researchers. The development of a pan-Canadian framework for action addressing the integration and coordination of technology in mental health services can also guide initiatives in this field. Our findings provide a prepandemic baseline and replicable methods to monitor how the use of technology-supported services and innovations emerge relative to other priorities in MHA policy during and after the COVID-19 pandemic.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,089 | 0,289 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,035 | 0,041 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».