Expression of concerns: Evaluating the importance of nurse competencies (doi: 10.17816/socm634585)
Bibliographic record
Abstract
An article by L.I. Kaspruk entitled “Evaluating the Importance of Nurse Competencies” has been published in the Sociology of Medicine journal (doi: 10.17816/socm634585). This article is an appeal from the Editorial Board of the journal to the readership, expressing concern about the impact that may result from the publication of the above article. The concern arises from the lack of information in the article about the ethical review of the research protocol involving human participants and the approval of the protocol by an ethics committee. Furthermore, the participants in the study were not randomly selected respondents but rather representatives of the professional medical community. Unfortunately, the authors were unable to provide the ethics committee’s approval of the research protocol or the conclusion that the study did not require ethical review. The Editorial Board of the Sociology of Medicine journal considers this practice of conducting studies and presenting their results unacceptable. However, the article was accepted for publication due to the high significance of the presented data. Henceforth, the Editorial Board will not accept for publication the results of original research that violates the principles of biomedical ethics.
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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".