Bibliographic record
Abstract
Various international health care organizations strongly recommend the implication of non-dental health care professionals in oral health. Consequently, nurses are currently often required to question their ability to manage patients’ oral health-related problems, as well as the very meaning of their management of patients in such situations. The purpose of this paper is to draw attention to the role of the nurse in the management of oral health. Professional associations and academic institutions involved in the development of nursing professional skills, and health policymakers involved in decisions concerning the geographical distribution of nursing personnel and adoption of laws in Quebec, act in a disconnected manner, independently from dental professionals and without taking into account the actual oral health care needs of the various categories of beneficiaries.The ever-growing elderly population, in a context of limited financial resources and other austerity measures, have contributed over time to diminishing access to oral health services, especially for vulnerable populations. Care orientation is fundamental to the nurse-patient relationship and nurses encounter many difficulties in addressing patients’ oral health-related needs, leading to various ethical and deontological implications. In the multidisciplinary environment of the health care system, it is therefore necessary to support nurses in their clarification of their contemporary role in the oral health of their patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".