Bibliographic record
Abstract
In an effort to optimize healthcare, people (that is, patients, providers, and funders) have started to increasingly scrutinize nurses' practices, whether these relate to assessments, plans of care, interventions, or evaluations.What people want to know is, what is the utility, efficacy, and effectiveness of the services being recommended, delivered, and paid for?Do these interventions induce their intended outcomes, or do they cause more harm than good?This trend, which is commonly known as evidence-based practice, has challenged all healthcare practices.There are no sacred practices seemingly beyond the apparently critical gaze of evidence-based practice.From this lens, everything should be subject to critique and critical review.While the foregoing aim of evaluating everything seems ideal, it is not as comprehensive in practice.What is lost in this new movement is the actual practice of critical appraisal.In decrying the previous approach of providing care based on anecdotal evidence, personal opinion, and historical practice trends, we have failed to maintain the foundational premise of critical reflection: to question and challenge everything.Indeed, the basic tenets are simple: question that which we are told is best; ask how and why one item has been established as ideal; and challenge the prevailing norms, so as to fulfill our professional obligations of providing patient care that achieves patients' goals.Such critical thinking, which should be a central aspect of evidence-based practice, however, is often applied to everything but evidence-based practice itself.Reflecting on if, how, when, and in what ways evidence-based practice helps, including a review of how evidence-based practice is implemented, is required.What appears to have happened is that, in opposition to a previous mindset, wherein initiatives were simply applied, we now seem to be unable to provide care until the so-called right type of evidence emerges.We have become dangerously immobilized in the absence of evidence.But does this make sense?Is this actually a change in how we care for patients?It seems to be little more than the replacement of one dogma with another.Instead of practicing without evidence (what I suggest is the previous modus operandi), we now simply do nothing when there is no evidence (the current approach).How is this different from before though?Does this new approach actually improve patient care, or does it simply create a new form of practice that is equally devoid of critically thinking?.Now, it simply seems that we have becoming unable, or at least increasingly reluctant, to address inequities and suffering due to a paucity of allegedly Éditorial/
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.069 | 0.047 |
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".