How to Measure Client Satisfaction With Stop Smoking Services: A Pilot Project in the UK National Health Service
Bibliographic record
Abstract
Abstract This pilot study aimed to develop a tool and methodology for measuring client satisfaction in UK National Health (NHS) Stop Smoking Services (SSS). A brief postcard questionnaire (measuring overall satisfaction with the service, willingness to recommend the service to others and smoking status) and a complete questionnaire (with 20 additional items measuring satisfaction with specific elements of the service) were developed. An NHS SSS mailed the postcard to 298 clients who had set a quit date in the previous quarter, they mailed the complete questionnaire to a subsample of 99 clients. Overall 34% (100/298) of those surveyed responded: 30% (90/298) for the card and 25% (25/99) for the questionnaire (15 people responded to both). Intraclass correlation coefficients (ICC) were found to be acceptable for both the overall service satisfaction item (ICC value = .43, p = .05) and the item regarding recommending the service to others (ICC-value = .83, p < .001). Hence the tool had reliability and at least face validity and the survey methodology proved practicable. The small modifications made to service delivery and the need for future research are discussed.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".