Artificial intelligence is set to transform mental health services
Bibliographic record
Abstract
The current development in the field of artificial intelligence and its applications has advantages and disadvantages in the digital age that we now live in. The state of the use of AI for mental health has to be assessed by stakeholders, which includes all of us. We must comprehend the trends, gaps, opportunities, challenges, and shortcomings of this new technology. As the field evolves, rules, regulatory frameworks, guidelines, standards, and policies will develop and will progressively scale upwards. To advance the field, mental health professionals must be prepared to meet obstacles and seize possibilities presented by creative and disruptive technologies like AI. Therefore, a collaborative strategy must include multi-stakeholder participation in basic, translational, and clinical aspects of AI. Mental health practitioners need to be ready to face challenges and embrace and harness the power of innovative and disruptive technology such as AI that could offer to move the field forward.
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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".