Delivery of compassionate mental health care in a digital technology-driven age: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: As digital technologies become an integral part of mental health care delivery, concerns have risen regarding how this technology may detract from health professionals' ability to provide compassionate care. To maintain and improve the quality of care for people with mental illness, there is a need to understand how to effectively incorporate technologies into the delivery of compassionate mental health care. The objectives of this scoping review are to: (1) identify the digital technologies currently being used among patients and health professionals in the delivery of mental health care; (2) determine how these digital technologies are being used in the context of the delivery of compassionate care and (3) uncover the barriers to, and facilitators of, digital technology-driven delivery of compassionate mental health care. METHODS AND ANALYSIS: Searches were conducted of five databases, consisting of relevant articles published in English between 1990 and 2019. Identified articles will be independently screened for eligibility by two reviewers, first at a title and abstract stage, and then at a full-text level. Data will be extracted and compiled from eligible articles into a data extraction chart. Information collected will include a basic overview of the publication including the article title, authors, year of publication, country of origin, research design and research question addressed. On completion of data synthesis, the authors will conduct a consultation phase with relevant experts in the field. ETHICS AND DISSEMINATION: Ethical approval is not required for this scoping review. With regards to the dissemination plan, principles identified from the relevant articles may be presented at conferences and an article will be published in an academic journal with study results. The authors also intend to engage interested mental health professionals, health professional educators and patients in a discussion about the study findings and implications for the future.
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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 | 0.120 | 0.106 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.018 |
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".