<scp><i>Show me you care</i></scp>: A patient‐ and family‐reported measure of care experiences in early psychosis services
Bibliographic record
Abstract
AIM: Despite their emphasis on engagement, there has been little research on patients' and families' experiences of care in early intervention services for psychosis. We sought to compare patients' and families' experiences of care in two similar early psychosis services in Montreal, Canada and Chennai, India. Because no patient- or family-reported experience measures had been used in a low- and middle-income context, we created a new measure, Show me you care. Here we present its development and psychometric properties. METHODS: Show me you care was created based on the literature and stakeholder inputs. Its patient and family versions contain the same nine items (rated on a 7-point scale) about various supportive behaviours of treatment providers towards patients and families. Patients (N = 293) and families (N = 237) completed the measure in French/English in Montreal and Tamil/English in Chennai. Test-retest reliability, internal consistency, convergent validity, and ease of use were evaluated. RESULTS: Test-retest reliability (intra-class correlation coefficients) ranged from excellent (0.95) to good (0.66) across the patient and family versions, in Montreal and Chennai, and in English, French, and Tamil. Internal consistency estimates (Cronbach's alphas) were excellent (≥0.87). The measure was reported to be easy to understand and complete. CONCLUSION: Show me you care fills a gap between principles and practice by making engagement and collaboration as central to measurement in early intervention as it is to its philosophy. Having been co-designed and developed in three languages and tested in a low-and-middle-income and a high-income context, our tool has the potential for global application.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".