Creating European guidelines for Chiropractic Incident Reporting and Learning Systems (CIRLS): relevance and structure
Bibliographic record
Abstract
BACKGROUND: In 2009, the heads of the Executive Council of the European Chiropractors' Union (ECU) and the European Academy of Chiropractic (EAC) involved in the European Committee for Standardization (CEN) process for the chiropractic profession, set out to establish European guidelines for the reporting of adverse reactions to chiropractic treatment. There were a number of reasons for this: first, to improve the overall quality of patient care by aiming to reduce the application of potentially harmful interventions and to facilitate the treatment of patients within the context of achieving maximum benefit with a minimum risk of harm; second, to inform the training objectives for the Graduate Education and Continuing Professional Development programmes of all 19 ECU member nations, regarding knowledge and skills to be acquired for maximising patient safety; and third, to develop a guideline on patient safety incident reporting as it is likely to be part of future CEN standards for ECU member nations. OBJECTIVE: To introduce patient safety incident reporting within the context of chiropractic practice in Europe and to help individual countries and their national professional associations to develop or improve reporting and learning systems. DISCUSSION: Providing health care of any kind, including the provision of chiropractic treatment, can be a complex and, at times, a risky activity. Safety in healthcare cannot be guaranteed, it can only be improved. One of the most important aspects of any learning and reporting system lies in the appropriate use of the data and information it gathers. Reporting should not just be seen as a vehicle for obtaining information on patient safety issues, but also be utilised as a tool to facilitate learning, advance quality improvement and to ultimately minimise the rate of the occurrence of errors linked to patient care. CONCLUSIONS: Before a reporting and learning system can be established it has to be clear what the objectives of the system are, what resources will be required and whether the implementing organisation has the capacity to operate the system to its full advantage. Responding to adverse event reports requires the availability of experts to analyse the incidents and to provide feedback in a timely fashion. A comprehensive strategy for national implementation must be in place including, but not limited to, presentations at national meetings, the provision of written information to all practitioners and the running of workshops, so that all stakeholders fully understand the purposes of adverse event reporting. Unless this is achieved, any system runs the risk of failure, or at the very least, limited usefulness.
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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 | 0.006 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".