Examining Interprofessional teams structures and processes in the implementation of a primary care intervention (Health TAPESTRY) for older adults using normalization process theory
Bibliographic record
Abstract
BACKGROUND: Many countries are engaged in primary care reforms to support older adults who are living longer in the community. Health Teams Advancing Patient Experience: Strengthening Quality [Health TAPESTRY] is a primary care intervention aimed at supporting older adults that involves trained volunteers, interprofessional teams, technology, and system navigation. This paper examines implementation of Health TAPESTRY in relation to interprofessional teamwork including volunteers. METHODS: This study applied Normalization Process Theory (NPT) and used a descriptive qualitative approach [1] embedded in a mixed-methods, pragmatic randomized controlled trial. It was situated in two primary care practice sites in a large urban setting in Ontario, Canada. Focus groups and interviews were conducted with primary care providers, clinical managers, administrative assistants, volunteers, and a volunteer coordinator. Data was collected at 4 months (June-July 2015) and 12 months (February-March 2016) after intervention start-up. Patients were interviewed at the end of the six-month intervention. Field notes were taken at weekly huddle meetings. RESULTS: Overall, 84 participants were included in 17 focus groups and 13 interviews; 24 field notes were collected. Themes were organized under four NPT constructs of implementation: 1) Coherence- (making sense/understanding of the program's purpose/value) generating comprehensive assessments of older adults; strengthening health promotion, disease prevention, and self-management; enhancing patient-focused care; strengthening interprofessional care delivery; improving coordination of health and community services. 2) Cognitive Participation- (enrolment/buy-in) tackling new ways of working; attaining role clarity. 3) Collective Action- (enactment/operationalizing) changing team processes; reconfiguring resources. 4) Reflective Monitoring- (appraisal) improving teamwork and collaboration; reconfiguring roles and processes. CONCLUSIONS: This study contributes key strategies for effective implementation of interventions involving interprofessional primary care teams. Findings indicate that regular communication among all team members, the development of procedures and/or protocols to support team processes, and ongoing review and feedback are critical to implementation of innovations involving primary care teams. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT02283723 November 5, 2014. Prospectively registered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".