How do registered nurses define rurality?
Bibliographic record
Abstract
OBJECTIVE: The objective of this analysis was to identify the meaning of rurality for registered nurses (RNs) practising in rural and remote Canada. SETTING AND DESIGN: An existing Statistics Canada definition was used to stratify Canada's 10 provinces into urban and rural areas. As part of a national multi-method study, a random sample of RNs in these rural strata, plus all RNs working in outpost settings and northern territories, were surveyed concerning the nature of nursing practice. Content analysis was used to identify themes from an open-ended question: 'How do you define rural/remote?' Refinement of the themes was conducted by the survey team and credibility was supported through investigator triangulation. PARTICIPANTS: Of the 3933 RNs who responded to the survey (68% response rate), 3412 provided a definition of rural/remote. A subsample of 1285 RNs was used for detailed thematic analysis because these respondents provided definitions with a clear referent to rural and/or to remote; the remaining sample was used for verification of themes. RESULTS: Four defining themes were identified by RNs for both rural and remote: community characteristics, geographical location, health human and technical resources, and nursing practice characteristics. CONCLUSIONS: The themes can be used as content domains or dimensions of rurality to improve our understanding of how to describe rural communities, including geographical location and nursing practice, from the perspective of RNs.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".