Factors that may be influencing the rise in prescription testosterone replacement therapy in adult men: a qualitative study
Bibliographic record
Abstract
Objective: To explore and describe the factors that may be influencing the rise of prescribing and use of testosterone replacement therapy (TRT) in adult men. Design: A rapid qualitative research design using semi-structured interviews with providers and patients. Setting: Ontario, Canada. Participants: Nine men who have used TRT (referred to as “patients”), and six primary care clinicians and seven specialists (collectively referred to as “providers”) who prescribed or administered TRT. Method: Patients’ and providers’ perspectives were investigated through semi-structured interviews. A purposive sampling approach was used to recruit all participants. We conducted qualitative analysis using the framework approach for applied health research. Main findings: Participants perceived the following factors to have influenced TRT prescriptions and use in adult men: provider factors (diagnostic ambiguity of age-related hypogonadism and beliefs about appropriateness of TRT) and patient factors (access to information on TRT and drug seeking behavior). They perceived that these factors have perpetuated a rise in prescription in the absence of clear clinical guidelines and unclear research evidence on the safety and efficacy of TRT. Conclusion: The findings of this study highlight that much work still needs to be done to improve diagnostic accuracy and encourage appropriate TRT prescription in adult men. In addition, both patients and providers need more information about the risks and long-term effects of TRT in men.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".