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Record W2418012970 · doi:10.1177/082585970702300304

Developing Rural Communities’ Capacity for Palliative Care: A Conceptual Model

2007· article· en· W2418012970 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Palliative Care · 2007
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsLakehead University
Fundersnot available
KeywordsPalliative careNursingHealth careRural areaMedicineEconomic growth

Abstract

fetched live from OpenAlex

The population in Canada and other developing countries is aging, increasing the need for palliative care services. In rural communities, care of dying people is normally provided by health care professionals as part of a generalist practice, not by palliative care specialists. Despite a lack of specialists and resources, some rural communities have developed local palliative care programs. The goal of this research was to conceptualize rural communities' process of developing palliative care programs using a theoretical perspective of community capacity development. Data were from nine focus groups of interdisciplinary rural health care providers who provided palliative care in seven provinces/territories of Canada. The outcome is a theoretical model that conceptualizes the process of developing palliative care programs in four sequential phases: antecedent community conditions, a catalyst, creating the team, and growing the program. The activities of each phase are outlined. This research offers practical and theoretical knowledge to guide practitioners and planners seeking to develop palliative care programs in other rural communities.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.304
GPT teacher head0.456
Teacher spread0.151 · 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