Discharge planning at rural and small town hospitals: how is it accomplished?
Bibliographic record
Abstract
Within Canadian society inequities exist in health status and health care provision. Residents of rural, isolated areas tend to fare worse when compared with their urban counterparts. Much of the research about health care provision is written for and from urban centres. However, the research in this thesis is not urban-based and addresses an issue important to rural communities. The research explores how discharge planning is accomplished at rural and small town acute care hospitals within the Northern Interior Health Region of British Columbia. A descriptive qualitative methodology was used to address the research question. Fifteen semi-structured interviews were conducted at five different small town locations within the Northern Interior Health Region of British Columbia. At each location interviews were conducted with three key informants about the discharge planning practices utilized at the local hospital. These key informants were a practicing physician, a nurse employed at the hospital, and a recently discharged patient. Analysis of the interview transcripts revealed twenty-four themes. Eleven of these themes suggest that the rural and small town environment exerts a positive influence on discharge planning processes, practices, and outcomes. Thirteen of the emergent themes suggest that the rural and small town context negatively influences discharge planning. A comparative analysis across key informant groups found similarities across the groups with physician and nurse responses being most similar. The themes derived from the patient interviews were more unique. Results suggest that improving outcomes for patients discharged from rural and small town hospitals will require augmentation and coordination of community based supports such as home care nursing, mental health services, and social services. Increased accessibility to specialist resources and more direct involvement of patients in planning for their post-hospital care needs are also indicated.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".