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Record W3157718418 · doi:10.1093/bjs/znaa087

Patient engagement study to identify and improve surgical experience

2021· article· en· W3157718418 on OpenAlexafffund
Erin Kennedy, Marg McKenzie, Selina Schmocker, Lianne Jeffs, Michael D. Cusimano, Amandeep Pooni, Rahima Nenshi, Adena Scheer, T.L. Forbes, Robin S. McLeod

Bibliographic record

VenueBritish journal of surgery · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityPublic Health OntarioSinai Health SystemSt. Michael's HospitalMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineWorksheetPatient experienceMedical educationNursingHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Patient engagement is the establishment of active partnerships between patients, families, and health professionals to improve healthcare delivery. The objective of this project was to conduct a series of patient engagement workshops to identify areas to improve the surgical experience and develop strategies to address areas identified as high priority. METHODS: Faculty surgeons and patients were invited to participate in three in-person meetings. Evaluation included identifying and developing strategies for three priority areas to improve the surgical experience and level of engagement achieved at each meeting. RESULTS: Sixteen faculty surgeons and 32 patients participated. Some 63 themes to improve the surgical experience were identified; the three highest-priority themes were physician communication, discharge process, and expectations at home after discharge. Individual improvement strategies for these three prioritized themes (12, 36 and 6 respectively) were used to develop a formal strategic plan, and included a physician communication survey, discharge process worksheet and video, and guideline regarding what to expect at home after discharge. Overall, the level of engagement achieved was considered high by over 85 per cent of the participants. CONCLUSION: A high level of patient engagement was achieved. Priorities were identified with patients and surgeons to improve surgical experience, and strategies were developed to address these areas.

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)0.002
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 score0.194
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.246
GPT teacher head0.455
Teacher spread0.209 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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