ShareDisk: A novel visual tool to assess perceptions about who should be responsible for supporting persons with mental health problems
Bibliographic record
Abstract
Objectives: Views on who bears how much responsibility for supporting individuals with mental health problems may vary across stakeholders (patients, families, clinicians) and cultures. Perceptions about responsibility may influence the extent to which stakeholders get involved in treatment. Our objective was to report on the development, psychometric properties and usability of a first-ever tool of this construct. Methods: We created a visual weighting disk called ‘ShareDisk’, measuring perceived extent of responsibility for supporting persons with mental health problems. It was administered (twice, 2 weeks apart) to patients, family members and clinicians in Chennai, India ( N = 30, 30 and 15, respectively) and Montreal, Canada ( N = 30, 32 and 15, respectively). Feedback regarding its usability was also collected. Results: The English, French and Tamil versions of the ShareDisk demonstrated high test–retest reliability ( rs = .69–.98) and were deemed easy to understand and use. Conclusion: The ShareDisk is a promising measure of a hitherto unmeasured construct that is easily deployable in settings varying in language and literacy levels. Its clinical utility lies in clarifying stakeholder roles. It can help researchers investigate how stakeholders’ roles are perceived and how these perceptions may be shaped by and shape the organization and experience of healthcare across settings.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".