Bibliographic record
Abstract
These approaches complement one another while adding to our overall understanding of rural nursing practice. For example, the RNDB had not previously been analysed with rural nurses in mind. The demographic profile of rural RNs was generated for the first time for Canada as a whole and for the individual provinces and territories.(18) The documentary analysis takes a critical view of the policy context within which rural nurses practise.(19) The national survey has collected an abundance of information about rural nurses' work, quality of work life and degree of work satisfaction. And the experiences that nurses relate in the narrative approach bring to life the challenges and rewards of working in a variety of rural settings. A national survey of RNs working in rural and remote areas has been completed using a mailed questionnaire and followup based on Dillman's tailored design method.(29) The 3,933 eligible respondents represent all provinces and territories and an overall response rate of 68 per cent after correcting for duplicate registrations, address problems, and ineligibility (e.g., living rural, working urban). Sampling was done in collaboration with the professional nursing associations of each province and territory, using the databases of all RNs with active registration, while maintaining anonymity and confidentiality. The sampling strategy was twofold. First, a stratified random sample was selected from RNs with rural addresses(30) in each of the 10 provinces. Second, the questionnaire was mailed to the total population of Canadian RNs who indicated on their registration forms that their primary workplace was a nursing station or outpost setting and to all RNs registered in the territories (as an attempt to capture "remote" areas). Based on a total population of 229,813 RNs in Canada,(31) with stratification by province and assuming that the ratio of rural/urban nurses was similar to the rural/urban population proportions in the provinces,(32) we determined that 3,500 rural nurses would provide estimates that are statistically significant (p <.05) nationally, with a 90 per cent confidence level provincially. Rural nurses have many reasons to celebrate: they provide care to individuals and families of all ages, for a variety of conditions, in a range of rural and remote settings. Although they work in environments that are primarily governed by urban-centric policies and in workplaces where the quality of work life is often limited, the nurses reveal their passion and dedication to their communities and to practising in rural and remote areas through their stories and their survey comments. The nature of rural and remote nursing is deceiving; its complexity is seen during the nurses' interactions, first as community members and then as professionals. Rural nurses are often charting new courses in their communities and workplaces. Muriel Strode said, "Do not follow where the path may lead. Go instead where there is no path and leave a trail."(33) Discovering the nature of nursing practice in rural and remote Canada through this national study is the first step on such a path, and hopefully the first of many \n \n \n \ntrails.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".