Le devis mixte en sciences infirmières ou quand une question de recherche appelle des stratégies qualitatives et quantitatives
Bibliographic record
Abstract
L’utilisation d’un devis mixte soulève de nombreuses interrogations notamment en regard de la position paradigmatique. En s’appuyant sur la vision pragmatique, il est possible de considérer le devis mixte comme étant la meilleure façon de répondre à une question de recherche donnée. Le choix de l’un des différents types de devis mixtes assure l’atteinte du but de l’étude. Afin d’illustrer l’utilisation d’un tel devis, une étude en sciences infirmières sert d’exemple. Les défis soulevés par un tel devis et la place de ce type de recherche en sciences infirmières sont discutés.
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 | 0.154 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".