Factors Influencing Patient Disclosure to Physicians in Birth Control Clinics: An Application of the Communication Privacy Management Theory
Bibliographic record
Abstract
The focus of the current study is whether, and why, female patients limit or alter their personal histories when discussing sensitive subject matter with their physician in birth control clinics. Fifty-six female patients (M = 21.6 years, SD = 3.05) completed anonymous questionnaires exploring their comfort with and ability to disclose personal histories in the immediately preceding interview with the physician. The present study used communication privacy management (CPM) as the theoretical lens through which to view the interaction. Approximately one-half of the sample (46%) reported limiting or altering information. Patients with a highly permeable privacy orientation, as evidenced by a history of open communication regarding sexual issues, were those who reported fully disclosing to their physicians. Of the physician characteristics considered to map onto patient privacy rules, the physician's gender, hurriedness, friendliness, use of a first-name introduction, and open-ended questions were significantly related to patients' reported ease in fully disclosing personal information (p < .05). This study presents a novel application of CPM and has implications for training medical students and for parent-child communication regarding sexual issues.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".