E-mental health: Updates on recent achievements and pitfalls
Bibliographic record
Abstract
The patient-centered care features quality, affordable, and timely care in a variety of settings – technology is a key part of that – particularly among younger generations and child and adolescent patients. The consumer movement related to new technologies is nearly passing clinicians by, as new ways of communicating with others (text, e-mail, Twitter, Facebook) revolutionizes how we experience life and access healthcare. This paper explores a continuum with healthy, innovative behavior on one end (e.g., social media) and pathological Internet use on the other end – and the range of self-help and e-mental healthcare options being used. Specifically, it focuses on how social media adds to, yet may complicate healthcare delivery, such that clinicians may need to adjust our approach to maintain therapeutic relationships, interpersonal/clinical boundaries, and privacy/confidentiality. We suggest planning ahead to discuss expectations about online communication between doctors and patients as part of the informed consent process, offer other do's and dont's for patients and clinicians, and review applicable guidelines. More research is needed on consumer and patient use of technology related to healthcare, as is an approach to basic and advanced measurement of outcomes. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".