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Record W3119552443 · doi:10.1186/s12913-020-06031-6

How do inner and outer settings affect implementation of a community-based innovation for older adults with a serious illness: a qualitative study

2021· article· en· W3119552443 on OpenAlexafffundabout
Grace Warner, Emily Kervin, Barb Pesut, Robin Urquhart, Wendy Duggleby, Taylor G. Hill

Bibliographic record

VenueBMC Health Services Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of AlbertaOkanagan University CollegeMount Saint Vincent UniversityUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaDalhousie University
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCancer Research Institute
KeywordsImplementation researchNursing researchHealth administrationQualitative researchMedicineHealth careHealth informaticsPublic healthContext (archaeology)NursingPalliative carePolitical sciencePsychological interventionSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Implementing community-based innovations for older adults with serious illness, who are appropriate for a palliative approach to care, requires developing partnerships between health and community. Nav-CARE is an evidence-based innovation wherein trained volunteer navigators advocate, facilitate community connections, coordinate access to resources, and promote active engagement of older adults within their communities. Acknowledging the importance of partnerships between organizations, the aim of our study was to use the Consolidated Framework for Implementation Research (CFIR) to explore organizational (Inner Setting) and community or health system level (Outer Setting) barriers and facilitators to Nav-CARE implementation. METHODS: Guided by CFIR, qualitative individual and group interviews were conducted to examine the implementation of Nav-CARE in a Canadian community. Participants were individuals who delivered or managed Nav-CARE research, and stakeholders who provided services in the community. The Framework Method was used to analyse the data. Particular attention was paid to the host organization's external network and community context. RESULTS: Implementation was affected by several inter-related CFIR domains, making it difficult to meaningfully separate key findings by only inner and outer settings. Thus, findings were organized into themes informed by CFIR, that cut across other domains and incorporated inductive findings: intraorganizational perceptions of Nav-CARE; public and healthcare professionals' perceptions of palliative care; interorganizational partnerships and relationships; community and national-level factors that should have facilitated Nav-CARE implementation; and suggested changes to Nav-CARE. Themes demonstrated barriers to implementing Nav-CARE, such as poor organizational readiness for implementation, and public and health provider perceptions palliative care was synonymous with fast-approaching death. CONCLUSIONS: Implementation science frameworks and theories commonly focus on assessing implementation of innovations within facilities and changing behaviours of individuals within that organizational structure. Implementation frameworks need to be adapted to better assess Outer Setting factors that affect implementation of community-based programs. Although applying the CFIR helped uncover critical elements in the Inner and Outer Settings that affected implementation of Nav-CARE. Our study suggests that the CFIR could expand the Outer Setting to acknowledge and assess organizational structures and beliefs of individuals within organizations external to the host organization who impact successful implementation of community-based innovations.

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 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)0.024
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0030.000
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 head0.381
GPT teacher head0.683
Teacher spread0.302 · 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 teacher head, not a consensus.

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

Citations23
Published2021
Admission routes3
Has abstractyes

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