Bibliographic record
Abstract
eMental Health is the use of information technology (ICT) to support and improve mental health; it includes online resources, social media and smartphone applications, as well as videotelephony. It used to be the new frontier, ungoverned but time has led to a maturity such that the novel is now commonplace and what was once Tomorrow's World is here today. From the experience of the networked Scandinavian countries, to the populations that novel techniques are reaching out to; QR codes in the UK, teens in Australia; from determining levels of Internet Addiction in Poland, to the use of that medium to treat anxiety disorders. An innovation from Law Enforcement has massive implications for patients recording consultations. Other experiments with risk management led to the failure of ‘Radar’, but paved the way for social care providers to develop safer systems that can care for large populations with few therapists. It is this use of Artificial Intelligence that may be the most challenging. Over 90 companies are developing the use of AI in diagnostics and related fields, with 14 US and Canadian hospitals involved with IBM's Watson. Will Drs become unnecessary? However the most innovative aspect of ICT in medicine is in research whether to greatly accelerate the process, or to ensure that educational tools genuinely answer patients’ questions. eHealth is an expanding field, that holds new promise, and opens question about who we are, what is our role, who do we care for and how; that today, ‘No man is an Island’, everyone should be connected. Disclosure of interest The author declares that he has no competing interest.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".