Examining students’ perception of rural practice following an educational strategy aimed at preparing postsecondary students for rural careers: a systematic review protocol for qualitative studies
Bibliographic record
Abstract
INTRODUCTION: Rural areas are widely acknowledged as being at a workforce disadvantage when compared with urban populations. One of the factors contributing to this disparity is the paucity of workforce professionals who live and practice in rural areas. Educational strategies used to train these workforce professionals may help better prepare students for rural careers and thus increase retention. The purpose of this systematic review is to examine students' perceptions of rural practice following an educational strategy used to prepare students for rural careers. METHODS AND ANALYSIS: Searches will be conducted in the following databases: Medline (Ovid), CINAHL (Ebscohost), ERIC (Proquest), Social Services Abstracts (Proquest), PsycINFO (Proquest) and IEEE Xplore. The literature search will be limited to articles published in English in the last 20 years. Data will be extracted for author(s), year of publication (2001-2021), country of origin, research question, research design, participants, where the study takes place (eg, classroom, community), educational strategies used, theoretical approach and findings related to the research question (ie, student perceptions). Methodological validity will be assessed using standardised tools. Two independent reviewers will conduct data extraction and quality appraisal, and any disagreement will be adjudicated by discussion or with a third reviewer. Results will be presented in tabular and narrative formats. ETHICS AND DISSEMINATION: This review does not require formal ethical approval as it does not involve direct student contact or student-identifiable data. The final systematic review will be submitted to a peer-reviewed journal.
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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 | 0.187 | 0.158 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 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".