A qualitative study of the determinants of adherence to NICE falls guideline in managing older fallers attending an emergency department
Bibliographic record
Abstract
BACKGROUND: The National Institute for Health and Care Excellence (NICE) 2004 Falls guideline was developed to improve the assessment and management of falls and prevention of future falls. However, adherence to the guideline can be poor. As emergency departments (EDs) are usually consulted by older adults (aged 65 and over) who experience a fall, they provide a setting in which assessments can be conducted or referrals made to more appropriate settings. The objective of this study was to investigate how falls are managed in EDs, reasons why guideline recommendations are not always followed, and what happens instead. METHODS: The study involved two EDs. We undertook 27 episodes of observation of healthcare professional interactions with patients aged 65 or over presenting with a fall, supported by review of the clinical records of these interactions, and subsequently, 30 interviews with healthcare professionals. The qualitative analysis used the framework approach. RESULTS: Various barriers and enablers (i.e. determinants of practice) influenced adherence at both EDs, including the following: support from senior staff; education; cross-boundary care; definition of falls; communication; organisational factors; and staffing. CONCLUSIONS: A variety of factors influence adherence to the Falls guideline within an ED, and it may be difficult to address all of them simultaneously. Simple interventions such as education and pro-formas are unlikely to have substantial effects alone. However, taking advantage of the influence of senior staff on juniors could enhance adherence. In addition, collaborative care with other NHS services offers a potential approach for emergency practitioners to play a part in managing and preventing falls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".