Taking care of yourself: a grounded theory study about how young women make a decision about having a Papanicolau test
Bibliographic record
Abstract
Newfoundland has one of the highest rates of cervical cancer in Canada and the western region of Newfoundland has the lowest cervical cancer screening rates. Young women, in particular, have a potential risk for cervical cancer. They initiate sexual intercourse at an early age, have an increased risk for sexually transmitted diseases and may not have the knowledge to access cervical cancer screening tests. The effectiveness of the Papanicolau test in reducing morbidity and mortality from cervical cancer is well accepted. The purpose of this study was to use a grounded theory approach to identify and describe the social processes that influence young women in western Newfoundland to make a decision about having a Papanicolau test. Interviews were conducted with 14 women, ages 19-29. Three main categories emerged from the data to show how these young women made decisions about having a Papanicolau test. Acquiring significant information was the first category. Using the information characterized the second category and the third category of 'changing perceptions' showed how young women's decision to incorporate having a Pap test into their regular health routine was reached. The central category that explained the relationship among all the categories was 'taking care of yourself'. -- The implications for nursing practice, nursing education and nursing research are included in the study findings.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".