Development and validation of the cervical cancer knowledge scale and HPV testing knowledge scale in a sample of Canadian women
Bibliographic record
Abstract
Knowledge of cervical cancer and HPV testing are important factors in proactive and continued engagement with screening and are critical considerations as countries move towards the implementation of HPV-based primary screening programs. However, existing scales measuring knowledge of both cervical cancer and HPV testing are not up to date with the current literature, lack advanced psychometric testing, or have suboptimal psychometric properties. Updated, validated scales are needed to ensure accurate measurement of these factors. Therefore, the aim of this study was to develop and validate two scales measuring cervical cancer knowledge and HPV testing knowledge. A pool of items was generated by retaining relevant existing items identified in a 2019 literature search and developing new items according to themes identified in recent systematic reviews. Items were assessed for relevance by the research team and then refined through seven cognitive interviews with Canadian women. A web-based survey including the remaining items (fourteen for each scale development) was administered to a sample of Canadian women in October and November of 2021. After data cleaning, N = 1027 responses were retained. Exploratory and Confirmatory Factor Analysis were conducted, and Item Response Theory was used to select items. The final cervical cancer knowledge scale (CCKS) and HPV testing knowledge scale (HTKS) were unidimensional, and each consisted of eight items. CFA demonstrated adequate model fit for both scales. The developed scales will be important tools to identify knowledge gaps and inform communications about cervical cancer screening, particularly in the context of HPV-based screening implementation.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".