Support for Carers of Young People with Mental Illness
Bibliographic record
Abstract
In this article, we show how a technology-mediated mental health therapy involving psycho-education, therapist moderators, and social networking can provide support for carers of young people with mental illness. This multi-faceted tool provides opportunities for users to adapt the system to their needs, leading us to refocus the goal of treatment adherence toward a relatively new phenomenon in HCI, concordance, which has not previously been examined in the HCI literature in relation to online mental-health tools. Concordance shares important links with the development of therapeutic alliance, which is centrally important to mental health therapy, and to Self-Determination Theory (SDT), which informed our approach to design. We present a three-month user study, which provides initial encouraging support for both the suitability of concordance as a lens for viewing user engagement and the idea that users can develop a therapeutic alliance with an online support system. This latter result is surprising as the phenomenon of therapeutic alliance generally describes a relationship between client and (human) clinician. Therapeutic alliance has previously been explored for face-to-face groups, and between individuals and online systems, but not for online groups. We show how even automated system behavior can encourage engagement from users and contribute to alliance formation, if the non-human parts of an online system are interactive. We argue that a design approach involving peer/moderator support as well as automated feedback, and which takes account of SDT, can provide support for therapeutic alliance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".