Bibliographic record
Abstract
The delivery of rural and remote healthcare has been identified in the literature as a unique and complex working environment for Nursing practice. This Canadian setting integrative review looks at barriers associated with rural and remote nursing. Nine articles were retained after filtering over 200 articles extracted from 4 databases. Critical Appraisal Skills Programme Checklist (CASP) for qualitative research and Quality Assessment Tool for Quantitative Studies (QATQ) were used for assessment of a total sample of N=3402 participants. Four (4) main themes (barriers) were extracted: 1) Professional Isolation, 2) Competing Demands, 3) Lack of Sustainable Continuing Educational Initiatives and 4) Lack of Organizational Support. Following analysis of the demographic data, an emerging theme of an aging workforce was also seen as a potential future barrier to rural nursing practice. Future research is required in order for sufficient and appropriate action to be taken in addressing aforementioned barriers. Recommendations for nursing practice and policy in rural and remote areas revolve around exposing nursing students to rural / remote settings, incentives for new graduate students to practice in these areas, as well as support and educational initiatives encouraging practitioners to work to their full scope of practice.
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".