The Financial Considerations of Employing a Dedicated Chaperone in Clinical Practice
Bibliographic record
Abstract
Introduction: Medical associations and medicolegal bodies are urging for increased chaperone use by physicians during intimate physical examinations in clinical practice (such as breast or pelvic examinations). However, widespread chaperone use is limited by factors such as staff availability and financial considerations. Presently, there is a scarcity of information available regarding the cost of hiring a dedicated chaperone. This study investigates the cost of hiring a chaperone and its financial implications for a physician's clinical practice. Materials and Methods: Using data from the Government of Canada website, the range of salary rates for clinic staff who can act as a chaperone in Canada was analyzed. The cost of hiring a chaperone was estimated to be in the range between the cost of hiring a minimum-wage worker and a nurse (the highest-paid hired medical office staff). Obstetrics and Gynecology as well as Plastic Surgery urban community practices were consulted regarding the costs of operating a clinic. Results: The approximate annual income for a minimum-wage worker in Canada is $29,250 CAD. Registered nurses earn on average $72,783.75 CAD per year. The cost of operating a private clinic practice with one staff member in Canada is on average $102,500 CAD per year. Thus, hiring an additional full-time chaperone could increase clinic expenses by approximately 49% per year, bringing the clinic cost to approximately $153,517 CAD per year. For part-time employment, the annual cost of hiring a chaperone is approximately $10,203 CAD for each day/week of employment. Conclusion: In terms of financial considerations, hiring a chaperone can increase clinic expenses by approximately one-and-a-half times. The findings of this study provide an important reference for physicians and may assist with the decision to employ chaperones in clinical practice.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".