Referee report. For: Identifying ways to maximise cervical screening uptake: a qualitative study of GPs’ and practice nurses’ cervical cancer screening-related behaviours [version 1; peer review: 1 approved]
Bibliographic record
Abstract
Background: Cervical screening uptake is declining in several countries. Primary care practitioners could play a greater role in maximising uptake, but better understanding is needed of practitioners’ cervical screening-related behaviours. Among general practitioners (GPs) and practice nurses, we aimed to identify cervical screening-related clinical behaviours; clarify practitioners’ roles/responsibilities; and determine factors likely to influence clinical behaviours. Methods: Telephone interviews were conducted with GPs and practice nurses in Ireland. Interview transcripts were analysed using the Theoretical Domains Framework (TDF), a comprehensive psychological framework of factors influencing clinical behaviour. Results: 14 GPs and 19 practice nurses participated. Key clinical behaviours identified were offering smears and encouraging women to attend for smears. Smeartaking responsibility was considered a predominantly female role. Of 12 possible theoretical domains, 11 were identified in relation to these behaviours. Those judged to be the most important were beliefs about capabilities; environmental context and resources; social influences; and behavioural regulation. Difficulties in obtaining smears from certain subgroups of women and inexperience of some GPs in smeartaking arose in relation to beliefs about capabilities. The need for public health education and reluctance of male practitioners to discuss cervical screening with female patients emerged in relation to social influences. Conclusions: We identified - for the first time - primary care practitioners’ cervical-screening related clinical behaviours, their perceived roles and responsibilities, and factors likely to influence behaviours. The results could inform initiatives to enable practitioners to encourage women to have smear tests which in turn, may help increase cervical screening uptake.
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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 | 0.017 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.352 | 0.109 |
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".