Support Needs Approach for Patients (SNAP) tool: a validation study
Bibliographic record
Abstract
OBJECTIVES: Patient-identified need is key to delivering holistic, supportive, person-centred care, but we lack tools enabling patients to express what they need to manage life with a long-term condition. The Support Needs Approach for Patients (SNAP) tool was developed to enable patients with advanced chronic obstructive pulmonary disease (COPD) identify and express their unmet support needs to healthcare professionals (HCPs), but its validity is unknown. This study aimed to establish face, content and criterion validity of the SNAP tool. DESIGN: Two-stage mixed-methods study involving patients with advanced COPD and their carers. Stage 1: Face and content validity assessed though focus groups involving patients and carers considering appropriateness, relevance and completeness of the SNAP tool. Data were analysed using conventional content analysis. Stage 2: Content and criterion validity assessed in a postal survey through patient self-completion of the SNAP tool and disease impact measures (Chronic Respiratory Questionnaire, COPD Assessment Test, and Hospital Anxiety and Depression Scale). Content validity assessed using summary statistics; criterion validity via correlations between tool items and impact measures. SETTINGS AND PARTICIPANTS: Two hundred and forty patients and carers participated. Stage 1 patient and informal carer participants were recruited from two primary care practices and Stage 2 patients from 28 practices. Participating practices located in the East of England were recruited via the NIHR Clinical Research Network: Eastern. RESULTS: Patients and carers found the tool patient-friendly and comprehensive, with potential clinical utility. No tool items were redundant. Clear correlations were found between tool items and the majority of items in the impact measures. CONCLUSIONS: The SNAP tool has good face, content and criterion validity. It has potential to support the delivery of holistic, supportive, person-centred care by enabling patients to identify and express their unmet support needs to HCPs.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".