Positive Outcomes: Validity, reliability and responsiveness of a novel person‐centred outcome measure for people with HIV
Bibliographic record
Abstract
OBJECTIVES: Despite successful treatment, people living with HIV experience persisting and burdensome multidimensional problems. We aimed to assess the validity, reliability and responsiveness of Positive Outcomes, a patient-reported outcome measure for use in clinical practice. METHODS: In all, 1392 outpatients in five European countries self-completed Positive Outcomes, PAM-13 (patient empowerment), PROQOL-HIV (quality of life) and FRAIL (frailty) at baseline and 12 months. Analysis assessed: (a) validity (structural, convergent and divergent, discriminant); (b) reliability (internal consistency, test-retest); and (c) responsiveness. RESULTS: An interpretable four-factor structure was identified: 'emotional wellbeing', 'interpersonal and sexual wellbeing', 'socioeconomic wellbeing' and 'physical wellbeing'. Moderate to strong convergent validity was found for three subscales of Positive Outcomes and PROQOL (ρ = -0.481 to -0.618, all p < 0.001). Divergent validity was found for total scores with weak ρ (-0.295, p < 0.001). Discriminant validity was confirmed with worse Positive Outcomes score associated with increasing odds of worse FRAIL group (4.81-fold, p < 0.001) and PAM-13 level (2.28-fold, p < 0.001). Internal consistency for total Positive Outcomes and its factors exceeded the conservative α threshold of 0.6. Test-retest reliability was established: those with stable PAM-13 and FRAIL scores also reported median Positive Outcomes change of 0. Improved PROQOL-HIV score baseline to 12 months was associated with improved Positive Outcomes score (r = -0.44, p < 0.001). CONCLUSIONS: Positive Outcomes face and content validity was previously established, and the remaining validity, reliability and responsiveness properties are now demonstrated. The items within the brief 22-item tool are designed to be actionable by health and social care professionals to facilitate the goal of person-centred care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".