Study characteristics and methods in a national nursing and allied health research programme: A descriptive study
Bibliographic record
Abstract
AIM: To describe the characteristics, methods and publications of studies funded by a French research programme for nursing and allied health professions. BACKGROUND: In some countries, the emerging field of nursing and allied health research is supported by funding programmes that are poorly documented. DESIGN: Observational descriptive study. METHODS: Data from funded studies in 2012-2020 were identified in publications, registries or an institutional repository by two authors in April-May 2022. Methods (quantitative, qualitative, mixed-method), study designs using the mixed-methods appraisal tool and thematic categories were extracted. RESULTS: In total, 212 studies were funded. The yearly number of studies has risen from 19 in 2012 to 33 in 2020. Eight nursing and allied health professions were represented among grant recipients, including 42% (89/212) of nurses. Half of the studies with identified methods were multicentric (91/185, 16% in 2012 and 71% in 2018). The median funding per study has risen from €63,471 in 2012 to €251,665 in 2020. The predominant methods were quantitative and interventional (144/185, 78%), with randomised controlled trials being the most frequent study design (125/144, 87%). Among the 38 quantitative observational studies, 14 (37%) were diagnostic studies. Three qualitative or mixed-methods studies were reported. The thematic categories included rehabilitation, nursing care, complementary medicine, patient education and metrology. Of the 50 completed studies, 18 publications were identified. CONCLUSIONS: The number of studies, multicentric studies and funding appeared to increase over the nine-year study period. The predominance of high-level evidence studies is encouraging, although a limited number of studies have been published. IMPLICATIONS FOR NURSING AND HEALTH POLICY: This study highlights the gradual involvement in a nursing and allied health research programme and the support needed to disseminate research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".