Consensus derived client outcomes and clinician actions for youth online chat mental health services: a Delphi study
Bibliographic record
Abstract
Introduction Online chat services have increased mental health care access for young people (12–25 years), yet their effectiveness remains unclear. This is partly due to a lack of consensus about primary client outcomes and clinician actions facilitating positive service outcomes. This study sought to identify (a) outcomes most important for young people accessing mental health support via online chat, and (b) clinician actions most relevant to achieving these outcomes. Method A comprehensive list of potential outcomes and actions was developed through literature review and consultation with youth online chat service providers. A three-round Delphi study was conducted with three panels of youth, researchers, and clinicians ( n = 100; 84% retention rate), primarily from Australia and Ireland. Consensus was reached if ≥75% of participants within at least two panels rated an outcome/action as very important or essential. Results Eleven client outcomes reached consensus: Feeling heard and validated; Reduced distress; Increased help-seeker capacity; Feeling safe; Optimism and hope; Connection with clinician and service; Feeling better; Reduced hopelessness; Reduced overwhelm; Increased coping; and Goals, answers and direction . Fifteen clinician actions reached consensus: Manage risk; Respect diversity; Validation; Welcoming environment; Active listening; Manage distress; Compassion; Checking in; Give choice; Youth friendly; Set expectations and focus; Provide resources; Holistic approach; Highlight strengths; and Problem-solving. Conclusions The identified client outcomes and clinician actions offer preliminary guidance for monitoring and evaluating youth online chat support. Future research should test and refine these domains within service contexts to inform robust measurement tool development for evaluating youth online chat services.
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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 | 0.075 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".