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Record W4396496087 · doi:10.35680/2372-0247.1811

Co-designing of Patient Safety Incident Disclosure Process in Primary Healthcare System in Qatar

2024· article· en· W4396496087 on OpenAlexaboutno aff
Nawal Khattabi, Amal Al Ali, Mariam Abdul Malik

Bibliographic record

VenuePatient Experience Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsPatient safetyHealth careMedicinePatient experienceProcess (computing)Healthcare systemMedical emergencyPrimary careNursingFamily medicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The importance of disclosing a patient safety incident to the patient involved is recognized. In Qatar, there is no legal requirement for disclosure. The primary health care system in Qatar includes 30 health centers located around the country, managed by the Primary Health Care Corporation (PHCC). Over 63 nationalities of staff deliver care in the health centers, many coming from countries where a disclosure policy is not implemented, and staff would be reluctant to disclose an incident to a patient for fear of reprimand. Many patients who receive care in the health centers come from countries where the health system culture is not open and transparent with patients. PHCC seeks accreditation of the health centers by Accreditation Canada, which has a required organizational practice of disclosure of patient safety incidents. To maintain accreditation, and consistent with PHCC’s strategy to deliver patient and family-centered care, PHCC needed to develop and implement a disclosure policy and process. The policy and process were co-designed by clinical staff working in the health centers and patients, through a focus group and individual interviews. The resulting policy and process focused on communicating disclosure quickly by a multidisciplinary team, providing for quick access to healthcare services by the patient, and fully documenting the disclosure, using a newly developed electronic record. Staff training, coordination with incident reporting and analysis, and ongoing evaluation were key stages of the implementation. The disclosure process has been in place for five years, with only positive feedback from patients and no legal implications.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.405
Teacher spread0.363 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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