Bibliographic record
Abstract
Il est reconnu que les infirmieres praticiennes (IP) possedent des connaissances et des competences propres et qu’elles jouent un role dans la recherche en dirigeant ou en collaborant a des projets. Les infirmieres praticiennes sont des infirmieres autorisees qui ont suivi une formation supplementaire, souvent une maitrise, et qui sont en mesure d’exercer dans un champ de pratique plus large (Donald et al., 2010; Kaasalainen et al., 2010). Elles ont notamment la capacite d’etablir des diagnostics, d’ecrire des ordonnances, de commander des analyses en laboratoire et d’adresser les patients a un specialiste (Sangster-Gormley, 2016). La recherche prouve depuis 40 ans que les IP prodiguent des soins surs et efficaces (Horrocks, Anderson et Salisbury, 2002; Mundinger, Kane, Lenz et Trial, 2009; Sackett et al., 1974; Sangster-Gormley, Frisch et Schreiber, 2013). Jusqu’a present, la recherche effectuee par des IP au Canada a surtout porte sur les soins primaires (Burgess et Purkis, 2010; Heale, 2012; Roots et MacDonald, 2014; Russell et al., 2009; Sangster-Gormley, Martin-Misener et Burge, 2013; Way, Jones, Baskerville et Busing, 2001). Cependant, les infirmieres praticiennes exercent de plus en plus dans des domaines hautement specialises, y compris dans des contextes d’oncologie aigue ou communautaire (Stahlke Wall et Rawson, 2016). Cette evolution vers des champs de pratique specialises ouvre des horizons de recherche stimulants pour les IP au Canada, et fait ressortir l’importance de reconnaitre l’experience unique de ces dernieres en soins de sante. Le present article a pour but d’etudier les perspectives de recherche pour les IP, particulierement en oncologie, et de discuter brievement des activites de diffusion des connaissances.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.169 | 0.094 |
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".