Personality Types as Predictors of Breast Cancer Screening Compliance in Korean Patients: A Mixed-Method Approach
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to identify personality types that can influence breast cancer screening (BCS) compliance among Korean women with breast cancer using a mixed-method approach. METHODS: The participants consisted of 93 women who underwent surgery for breast cancer between July 2010 and March 2012. The demographic and medical characteristics of the participants were evaluated through structured interviews. To identify personality types, in-depth interviews were performed and the transcribed interviews were evaluated using interpretive phenomenological analysis. The participants were categorized into two groups (compliance and non-compliance) based on compliance with the Korean Breast Cancer Society recommendations for BCS. RESULTS: Five personality types were identified through phenomenological analysis. There were significant differences in the chi-square test results for the BCS compliance and non-compliance groups according to age (p=0.048), cancer stage (p<0.001), and personality types (p=0.018). Logistic regression showed that the odds ratio for compliance with BCS was 9.35 (p=0.01) for individuals with a cautious-organized personality type, 9.38 (p=0.02) for those with a cautious-dependent personality, and 10.58 (p=0.04) for those with a sensitive-downcast personality compared to those with a cautious personality type. CONCLUSION: Participants with cautious-organized, cautious-dependent, and sensitive-downcast personality types were less likely to follow the BCS recommendations than those with a cautious personality type. This study provides a basis for the future development of an effective questionnaire to investigate the personality types of individuals with breast cancer in order to predict compliance with BCS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".