Making an inventory of best practices recommendations and putting them at nurses' disposal
Bibliographic record
Abstract
Introduction: New communication tools – in particular internet –\nprovide access to guidelines. The strength of the evidence supporting\nspecific recommendations and their intellectual independence is not\nalways guaranteed. Lack of time, lack of skills in critical appraisal\nor understanding of research language or other tools are mainly\nresponsible for the fact that professionals make their own ‘local’ recommendations,\nonly based on experts’ opinions. The aim of this\nstudy is to select best practice evidence-based guidelines and to put\nthem at the nurses’ disposal in their own language.\nMaterials and Method: The method was essentially based on literature\nreview of best practice guidelines validated by nurses’ associations\n(Finland, Ontario, CBO, HAS), the Cochrane Collaboration\nLibrary, Medline, CINAHL, BNI. Only the guidelines of the last\n7 years has been be explored. Each guideline has been assessed by\nAGREE, Shaneyfelt, Grilli & Cluzeau tools. Additional criteria have\nbeen used to assess the quality of the guidelines. All steps of guidelines\nselection were performed by two researchers. To put the recommendation\nat the nurses’ profession disposal a website has been\ndeveloped.\nResults: Six main topics have been developed: disorientation,\nnausea and vomiting, wound care, sedation, diabetes, nutrition. A\nspecial attention was focused on geriatric care at hospital. For each\ntopic, four guidelines are selected based on appraisal tools and\nadditional criteria. The website was accessible since July 2009 at the\nURL address: http://www.sesa.ucl.ac.be/guidelines. According the\nsatisfaction study, all users would recommend the website to a colleague\nand 9 respondents of 10 found the website very useful.\nConclusion: The website is a tool in order to have an access at\nthe evidence based information. This is a first step of a long process.\nThe website was considered very useful by users. But, a next question\nis the implementation the evidence based guidelines in clinical\nwork. The obstacles and support factors of evidence based guidelines\nin the geriatric care by nurses must be studied.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".