MétaCan
Menu
Back to cohort
Record W4320918687 · doi:10.3390/healthcare11040565

Gender Differences among Healthcare Providers in the Promotion of Patient-, Person- and Family-Centered Care—And Its Implications for Providing Quality Healthcare

2023· review· en· W4320918687 on OpenAlexaff
Sarah Ashley Lim, Amir Khorrami, Richard J. Wassersug, James R. Agapoff

Bibliographic record

VenueHealthcare · 2023
Typereview
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHealth careFamily centered careQuality (philosophy)Promotion (chess)NursingPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.702
GPT teacher head0.536
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHealthcareSame topicPatient-Provider Communication in HealthcareFrench-language works237,207