Understanding interprofessional collaboration in the context of chronic disease management for older adults living in communities: a concept analysis
Bibliographic record
Abstract
AIM: To report a concept analysis of interprofessional collaboration in the context of chronic disease management, for older adults living in communities. BACKGROUND: Increasing prevalence of chronic disease among older adults is creating significant burden for patients, families and healthcare systems. Managing chronic disease for older adults living in the community requires interprofessional collaboration across different health and other care providers, organizations and sectors. However, there is a lack of consensus about the definition and use of interprofessional collaboration for community-based chronic disease management. DESIGN: Concept analysis. DATA SOURCES: Electronic databases CINAHL, Medline, HealthStar, EMBASE, PsychINFO, Ageline and Cochrane Database were searched from 2000 - 2013. METHODS: Rodgers' evolutionary method for concept analysis. RESULTS: The most common surrogate term was interdisciplinary collaboration. Related terms were interprofessional team, multidisciplinary team and teamwork. Attributes included: an evolving interpersonal process; shared goals, decision-making and care planning; interdependence; effective and frequent communication; evaluation of team processes; involving older adults and family members in the team; and diverse and flexible team membership. Antecedents comprised: role awareness; interprofessional education; trust between team members; belief that interprofessional collaboration improves care; and organizational support. Consequences included impacts on team composition and function, care planning processes and providers' knowledge, confidence and job satisfaction. CONCLUSION: Interprofessional collaboration is a complex evolving concept. Key components of interprofessional collaboration in chronic disease management for community-living older adults are identified. Implications for nursing practice, education and research are proposed.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| 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".