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Record W4229760804 · doi:10.24124/2002/bpgub252

Discharge planning at rural and small town hospitals: how is it accomplished?

2002· dissertation· en· W4229760804 on OpenAlexaffabout
Sandra June Harker

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsContext (archaeology)Qualitative researchRural areaSmall townNursingHealth careMedicineGeographySocioeconomicsSociologyPolitical science

Abstract

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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.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0110.005
Scholarly communication0.0070.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.268
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes2
Has abstractyes

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