Supporting population mental health and wellness during the COVID-19 pandemic in Canada: protocol for a sequential mixed-method study
Bibliographic record
Abstract
INTRODUCTION: The global COVID-19 pandemic has reported to have a negative impact on the mental health and well-being of individuals around the world. Mental health system infrastructure, primarily developed to support individuals through in-person care, struggled to meet rising demand for services even prior to COVID-19. With public health guidelines requiring the use of physical distancing during the pandemic, digital mental health supports may be one way to address the needs of the population. Despite this, barriers exist in promoting and supporting access to existing and emerging digital resources. Text messaging may address some of these barriers, extending the potential reach of these digital interventions across divides that may separate some vulnerable or disadvantaged groups from essential mental health supports. Building on an existing knowledge synthesis project identifying key digital resources for improved mental health, this research will establish low-tech connections to assess need and better match access to services for those who need it most. The aim of this study is to codesign a customised two-way texting service to explore need and better align access to mental health supports for Canadians located in Saskatchewan during the COVID-19 pandemic. METHODS AND ANALYSIS: This study will be completed in Saskatchewan, Canada. For this project, the RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework will be used to support three phases of a sequential mixed-method study. An advisory committee of Saskatchewan residents will guide this work with the study team. A 10-week service will be launched to connect individuals with appropriately suited digital mental health interventions through the use of text messaging. In phase 1, implementation and prototyping will be conducted with collaborative codesign for key elements related to features of an enrolment survey and initial messaging content. Phase 2 will focus on advancing the effectiveness of the service using quantitative user data. In phase 3, an embedding approach will be used to integrate both qualitative and quantitative data collected to understand the overall acceptability, satisfaction and perceived benefit of the text messaging service. Thematic analysis and descriptive statistics will be used as analytic methods. ETHICS AND DISSEMINATION: This study has received approval from the Research Ethics Board at the University of Saskatchewan. A knowledge dissemination plan has been developed that includes traditional academic approaches such as conference presentations, and academic publications, as well as mainstream approaches such as social media, radio and dissemination through the advisory committee.
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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 | 0.069 | 0.049 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.014 |
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".