Bibliographic record
Abstract
Epilepsy is the second most common neurological condition and can be associated with significant morbidity, premature mortality, and high resource use. Epilepsy is a spectrum disorder due its diverse presentation, making it challenging to manage. As a result, treatment gaps exist. Clinical practice guidelines should facilitate the care of people with epilepsy. While evidence exists that guidelines are effective in improving the quality of care in some clinical settings, this has not been demonstrated for epilepsy. The objectives of this thesis are to: 1) Identify gaps in epilepsy guidelines, 2) Determine barriers and facilitators to implementation of epilepsy guidelines, and 3) Develop a knowledge translation (KT) strategy to optimize dissemination and implementation of epilepsy guidelines. Several methods were used to achieve the study objectives. A systematic review of epilepsy guidelines was conducted to identify gaps. A mixed-methods approach (quantitative survey and focus groups) was used to identify the determinants of guideline use among neurologists. Based on the results of the study examining determinants of guideline use, a theory-based KT strategy was proposed to facilitate future implementation of epilepsy guidelines in clinical practice. The systematic review identified 63 guidelines for the care of epilepsy covering 19 populations/conditions. Gaps in the availability of guidelines for high priority areas (i.e. elderly) and significant heterogeneity in quality were identified. Despite the number of guidelines available for the care of people with epilepsy, use of these guidelines clinically is poor. Reasons for the poor implementation of these guidelines among neurologists (end-users of epilepsy guidelines) were identified here, and include: lack of knowledge, poor credibility, applicability and motivation, insufficient resources, and lack of clarity regarding the target users. A three-pillared KT strategy to overcome the barriers of guideline use, and leverage facilitators is proposed to improve implementation in clinical practice. This body of work provides novel evidence into the current state of epilepsy guidelines, and the factors that determine their use clinically. This novel insight helps bridge a knowledge gap while the KT strategy outlined here provides the tools required to move towards improving implementation of guidelines for the care of people with epilepsy internationally.
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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 | 0.108 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".