United Kingdom cervical cancer screening and the costs of time and travel
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to estimate the time and travel costs generated by women when attending for Papanicolaou (Pap) smear tests or colposcopy appointments in the United Kingdom, both absolutely and relative to the health service cost of the national cervical cancer screening programs. METHODS: Data were obtained from questionnaires completed by two samples of women participating in a three-center trial of management of low-grade abnormalities detected by screening (n = 1,106 for Pap smears and n = 1,203 for colposcopy appointments). Women were 20 to 59 years of age and resident in Grampian or Tayside, Scotland, or Nottingham, England. Questionnaire data were supplemented with sociodemographic information previously collected at the time of recruitment to the trial. RESULTS: The mean total time and travel costs per attendance at a smear test and at a colposcopy appointment were estimated to be 9.2 pounds and 27.4 pounds, respectively, averaged across the three trial areas (valued at 2002 prices). Statistically significant intercenter disparities in time and travel costs were identified, particularly with respect to colposcopy appointments. For these, time and travel costs in Nottingham were substantially less than those in Grampian and Tayside (22.9 pounds, 30.2 pounds, and 32.1 pounds, respectively). Time and travel costs amount to 26 and 33 percent, approximately, over and above the direct health service costs of the English and Scottish screening programs, respectively. CONCLUSIONS: The time and travel costs associated with participation in the UK cervical cancer screening programs are substantial and are not spatially uniform across the country.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".