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Record W4360779103 · doi:10.4103/jfmpc.jfmpc_1511_22

Comparison of patients’ perceptions of family physicians’ patient-centeredness between virtual and in-person clinical encounters: A cross-sectional study

2023· article· en· W4360779103 on OpenAlexafffund
Helena Piccinini‐Vallis, Joanna Zed, Julie Easley

Bibliographic record

VenueJournal of Family Medicine and Primary Care · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsMedicineCross-sectional studyPerceptionFamily medicinePathology

Abstract

fetched live from OpenAlex

Introduction: A clinician's patient-centeredness is a core construct of quality healthcare and is associated with several positive patient outcomes. This study aimed to compare patient-perceived patient centeredness between in-person and virtual clinical encounters during the coronavirus pandemic. Materials and Methods: Participants completed an online anonymous questionnaire pertaining to a recent clinical encounter. Patients of an academic family medicine teaching clinic scheduled for either an in-person or a virtual clinical encounter were recruited by phone over a two-month period. Using the patient-centered clinical method as a conceptual framework, patient-perceived patient centeredness was measured by the Patient-Perceived Patient-Centeredness Questionnaire-Revised (PPPC-R), consisting of 18 items that reflect three factors (healthcare process, context and relationship, and roles). Results: The sample consisted of 72 participants. There was no difference in the PPPC-R scores between participants who received in-person and those who received virtual care. However, the mean ranks for the PPPC-R total score and for all three factors were higher for participants who saw a family physician compared to participants who saw a family medicine learner. Conclusion: Family physicians provided similar quality healthcare, measured through a patient-perceived patient-centeredness lens, via both virtual and in-person appointments. These results support sustaining virtual care when deemed appropriate by both patient and clinician.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.165
GPT teacher head0.463
Teacher spread0.297 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes2
Has abstractyes

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