The impacts for social workers of providing social work services in rural northeastern Ontario communities.
Bibliographic record
Abstract
The purpose of this study is to explore the personal and professional experiences of rural social workers. This is a qualitative study with a constructivist grounded theory lens. This study utilizes a sample of four participants. The participants work in the social work field and live in northeastern Ontario rural communities. Data was collected by audio-taped individual semi-structured interviews and subsequent member checking. The question explored is, how do rural social workers experience rural social work practice? Results of the study reveal that rural social workers experience both challenges and strengths within their professional and personal lives. Three themes related to rural social work emerged through data analysis. The themes include rural social workers as unprepared for their work environments, rural social workers as having multiple roles and the strengths and challenges rural social workers face. This research provides insight into the experiences of rural social workers and recommendations for how to better prepare rural social workers. The study further provides information related to implications for social work education, social work practice and implications for future research. The implications for social work education include a recommendation that social work students be provided with a broad range of training in all areas, including rural social work practice. The implications for social work practice include that we must encourage and maintain social workers who demonstrate an interest in rural social work. Lastly, future research could be done regarding supervision and rural social workers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".