The `WikiGuidelines' smartphone application: Bridging the gaps in availability of evidence-based smartphone mental health applications
Bibliographic record
Abstract
Over the past decade, there have been massive advances in technology. These advances in technology have significantly transformed various aspects of healthcare. The advent of E-health and its influence on healthcare practice also implies that there is a paradigm shift in the way healthcare professionals work. Conventionally, healthcare professionals would have to refer to books and journals for updates in treatment algorithms, but with the advent of technology, they could access this information via the web or via various smartphone applications on the go. In the field of Psychiatry, one of the commonest mental health disorder to date, with significant morbidity and mortality is that of Major depressive disorder. Routinely, clinicians and healthcare professionals are advised to refer to standard guidelines in guiding them with regards to their treatment options. Given the high prevalence of conditions like Major Depressive Disorder, it is thus of importance that whatever guidelines that clinicians and healthcare professionals refer to are constantly kept up to date, so that patients could benefit from latest evidence based therapy and treatment. A review of the current literature highlights that whilst there are a multitude of smartphone applications designed for mental health care, previous systematic review has highlighted a paucity of evidence based applications. More importantly, current literature with regards to provision of treatment information to healthcare professionals and patients are limited to web-based interventions. It is the aim of this technical note to highlight a methodology to which the authors have conceptualized in the implementation of an evidence based mental health guideline applications, known as the `Wiki Guidelines' smartphone application. The authors hope to illustrate the algorithms behind the development of the application, and how it could be easily updated by the guidelines working group.
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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 | 0.034 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".