Priority Given to Technology in Government-Based Mental Health and Addictions Vision and Strategy Documents: Systematic Policy Review (Preprint)
Bibliographic record
Abstract
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.
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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 | 0.089 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.035 | 0.041 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".