Implementing wound care guidelines: observations and recommendations from the bedside.
Bibliographic record
Abstract
The successful implementation of wound care guidelines requires an appreciation for the frustrations experienced by nurses trying to incorporate these tools into clinical practice. These frustrations or barriers to best wound care practice implementation are examined from the perspective of: 1) the practice environment, which must be understood; 2) the potential adopters, predominantly nurses seeking the best fit between evidence and their clinical practice setting; and 3) the evidence-based innovation created to change wound care practice at the point of care. Barriers identified include lack of available resources, time constraints, prescriptive guidelines that incorrectly assume details of the practice environment, and wound care product confusion. Recommendations to facilitate implementation from the bedside are discussed and include expanding guidelines to incorporate detailed educational content and dissemination strategies that serve to increase relevancy to everyday practice. Additional suggestions include decreasing wound care product confusion by developing standardized, function-based product nomenclature and improving the quality of wound care research to increase nurses' confidence in the evidence and resultant recommendations. Resources currently used to develop guidelines also should be utilized to create accompanying educational material to support the transfer and uptake of knowledge.
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 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 | 0.017 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".