Gender Differences among Healthcare Providers in the Promotion of Patient-, Person- and Family-Centered Care—And Its Implications for Providing Quality Healthcare
Bibliographic record
Abstract
The concept of “patient-centered care” (PCC) emphasizes patients’ autonomy and is commonly promoted as a good healthcare practice that all of medicine should strive for. Here, we assessed how six medical specialties—pediatrics, OBGYN, orthopedics, radiology, dermatology, and neurosurgery—have engaged with PCC and its derivative concepts of “person-centered care” (PeCC) and “family-centered care” (FCC) as a function of the number of female physicians in each field. To achieve this, we conducted a scoping review of three databases—PubMed, CINAHL, and PsycInfo—to assess the extent that PCC, PeCC, FCC, and RCC were referenced by different specialties in the medical literature. Reference to PCC and PeCC in the literature correlates significantly with the number of female physicians in each field (all p < 0.00001) except for neurosurgery (p > 0.5). Pediatrics shows the most extensive reference to PCC, followed by OBGYN, with a significant difference between all disciplines (p < 0.001). FCC remains exclusively embraced by pediatrics. Our results align with documented cognitive differences between men and women that recognize gender differences in empathizing (E) versus systemizing (S) with females demonstrating E > S, which supports PCC/PeCC/FCC approaches to healthcare.
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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 | 0.023 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".