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Record W4400100916 · doi:10.1136/bmjoq-2023-002738

Development of a survey to support assessment of safety learning systems

2024· article· en· W4400100916 on OpenAlexafffundabout
Hassan Mahmoud, Sunita Mulpuru, Kednapa Thavorn, Daniel McIsaac, Alan J. Forster

Bibliographic record

VenueBMJ Open Quality · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsMcGill UniversityOttawa HospitalCanadian Red Cross SocietyUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsCLARITYCronbach's alphaConsistency (knowledge bases)Face validityPatient safetyRelevance (law)Quality (philosophy)PsychologySample (material)Medical educationApplied psychologyMedicineHealth careComputer sciencePsychometricsClinical psychologyArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Patient safety learning systems play a critical role in supporting safety culture in healthcare organisations. A lack of explicit standards leads to inconsistent implementation across organisations, causing uncertainty about their roles and impact. Organisations can address inconsistent implementation by using a self-assessment tool based on agreed-on best practices. Therefore, we aimed to create a survey instrument to assess an organisation's approach to learning from safety events. METHODS: The foundation for this work was a recent systematic review that defined features associated with the performance of a safety learning system. We organised features into themes and rephrased them into questions (items). Face validity was checked, which included independent pre-testing to ensure comprehensibility and parsimony. It also included clinical sensibility testing in which a representative sample of leaders in quality at a large teaching hospital (The Ottawa Hospital) answered two questions to judge each item for clarity and necessity. If more than 20% of respondents judged a question unclear or unnecessary, we modified or removed that question accordingly. Finally, we checked the internal consistency of the questionnaire using Cronbach's alpha. RESULTS: We initially developed a 47-item questionnaire based on a prior systematic review. Pre-testing resulted in the modification of 15 of the questions, 2 were removed and 2 questions were added to ensure comprehensiveness and relevance. Face validity was assessed through yes/no responses, with over 80% of respondents confirming the clarity and 85% the necessity of each question, leading to the retention of all 47 questions. Data collected from the five-point responses (strongly disagree to strongly agree) for each question were used to assess the questionnaire's internal consistency. The Cronbach's alpha was 0.94, indicating a high internal consistency. CONCLUSION: This self-assessment questionnaire is evidence-based and on preliminary testing is deemed valid, comprehensible and reliable. Future work should assess the range of survey responses in a large sample of respondents from different hospitals.

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)0.022
metaresearch head score (Gemma)0.001
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 score0.224
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.543
GPT teacher head0.646
Teacher spread0.103 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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