What Are the Characteristics of the Parish Nursing Research Literature and How Can it Inform Parish Nurse Practice and Research in Canada? A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Parish nursing is a specialized branch of professional nursing that promotes health and healing by integrating body, mind and spirit as a practice model. Parish nurses contribute to the Canadian nursing workforce by promoting individual and community health and acting as system navigators. Research related to parish nursing practice has not been systematically collated and evaluated. PURPOSE: This review seeks to explore, critically appraise and synthesize the parish nurse (PN) research literature for its breadth and gaps, and to provide recommendations for PN practice and research. METHODS: A scoping review was conducted using Levac and colleagues' procedures and Arksey and O'Malley's enhanced framework. The CINAHL, ProQuest and PubMed databases were comprehensively searched for original research published between 2008 and 2020. The final sample includes 43 articles. The Mixed Methods Appraisal Tool was used to critically assess literature quality. RESULTS: There is a significant gap in PN research from Canada and non-U.S. countries. Methodological quality is varied with weak overall reporting. The literature is categorized under three thematic areas: (1) practice roles of the PN, (2) role implementation, and (3) program evaluation research. Research that evaluates health promotion program interventions is prominent. CONCLUSIONS: More rigorous research methods and the use of reporting checklists are needed to support evidence-informed parish nursing practice. Building relationships among parish nurses, nursing researchers and universities could advance parish nursing research and improve evidence-based parish nursing practice. Research into the cost effectiveness, healthcare outcomes, and the economic value of PN practice is needed.
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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 | 0.061 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.071 | 0.105 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".