Discussion of Potentially Sensitive Topics With Young People
Bibliographic record
Abstract
OBJECTIVES: To identify modifiable factors that facilitate discussion of potentially sensitive topics between health care providers and young people at preventive service visits after Patient Protection and Affordable Care Act implementation. METHODS: We used data from a national internet survey of adolescents and young adults (13-26 years old) in the United States. Questionnaire construction was guided by formative research and Fisher's Information-Motivation-Behavioral Skills model. Those who had seen a regular health care provider in the past 2 years were asked about 11 specific topics recommended by national medical guidelines. Four multivariable regression models were used to identify independent predictors of discussions of (1) tobacco use, (2) drug and/or alcohol use, (3) sexually transmitted infections or HIV, and (4) the number of topics discussed. RESULTS: Fewer than half of young people reported having discussed 10 of 11 topics at their last visit. Predictors were similar across all 4 models. Factors independently associated with health discussions included the following: ever talked with a provider about confidentiality (4/4 models; adjusted odds ratio [aOR] = 1.85-2.00), ever had private time with a provider (1 model; aOR = 1.50), use of health checklist and/or screening questionnaire at last visit (4 models; aOR = 1.78-1.96), and time spent with provider during last visit (4 models). Number of years that young men had seen their regular provider was significant in 1 model. Other independent factors were positive youth attitudes about discussing specific topics (3/3 models) and youth involvement in specific health risk behaviors (3/3 models). CONCLUSIONS: Discussions about potentially sensitive topics between health care providers and young people are associated with modifiable factors of health care delivery, particularly provider explanations of confidentiality, use of screening and/or trigger questionnaires, and amount of time spent with their provider.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".