PS239. The ‘WikiGuidelines’ Smartphone application: Bridging the gaps in availability of evidence-based smartphone mental health applications
Bibliographic record
Abstract
Abstract Background: Over the past decade, there have been massive advances in technology. These advances in technology have significantly transformed various aspects of healthcare. With the advent of technology, healthcare professionals could access 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. Methodology: Along with the help of an international workgroup, a concise set of guidelines was developed. Making use of cross-platform techniques in smartphone application programming, the Wiki Guidelines application for doctors and patients were launched since December 2015. Results: Since inception to date, there has been a cumulative downloads of 32 and 12 for the Wiki Guidelines Application Doctor and Patient respectively. Conclusions: Harnessing the advances in E-Health made it possible for clinicians to have immediate access to guidelines that are developed. Any modification of guidelines could be done in real-time and clinicians will be able to keep abreast with the latest developments in treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.133 | 0.032 |
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".