Safety Incidents in the Primary Care Office Setting
Bibliographic record
Abstract
BACKGROUND: In the United Kingdom, 26% of child deaths have identifiable failures in care. Although children account for 40% of family physicians' workload, little is known about the safety of care in the community setting. Using data from a national patient safety incident reporting system, this study aimed to characterize the pediatric safety incidents occurring in family practice. METHODS: We undertook a retrospective, cross-sectional, mixed methods study of pediatric reports submitted to the UK National Reporting and Learning System from family practice. Analysis involved detailed data coding using multiaxial frameworks, descriptive statistical analysis, and thematic analysis of a special-case sample of reports. Using frequency distributions and cross-tabulations, the relationships between incident types and contributory factors were explored. RESULTS: Of 1788 reports identified, 763 (42.7%) described harm to children. Three crosscutting priority areas were identified: medication management, assessment and referral, and treatment. The 4 incident types associated with the most harmful outcomes are errors associated with diagnosis and assessment, delivery of treatment and procedures, referrals, and medication provision. Poor referral and treatment decisions in severely unwell or vulnerable children, along with delayed diagnosis and insufficient assessment of such children, featured prominently in incidents resulting in severe harm or death. CONCLUSION: This is the first analysis of nationally collected, family practice-related pediatric safety incident reports. Recommendations to mitigate harm in these priority areas include mandatory pediatric training for all family physicians; use of electronic tools to support diagnosis, management, and referral decision-making; and use of technological adjuncts such as barcode scanning to reduce medication errors.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".