Doing Meso-Level Integration: The cultural, relational, and communications practices that draw primary care closer to the broader system.
Bibliographic record
Abstract
Introduction: In alignment with the conference’s first theme, this paper draws on qualitative research to address issues of change management implementation at the meso-level in integration. It uses a case study of integration efforts as they played out during the first wave of the COVID-19 Omicron variant in Alberta, Canada to identify practical ways of doing organization-to-organization integration in Primary Care (PC). Background: While the existing literature describes three high-level approaches to enabling the integration of health and social systems in a range of contexts, it is unclear how stakeholders in PC and health system organizations might practically operationalize these approaches. How, specifically, should those stakeholders go about: enacting a pro-integration culture; doing the relationship work required for integration; and communicating to support integration? Put differently, what are the practical change management behaviours that give life to these three key approaches and make integration real? Approach: A subset of semi-structured interviews with key informant stakeholders was drawn from a larger study of policy and change management implementation under pandemic conditions in Alberta, Canada. Participants in these interviews were directly involved in efforts to integrate PC into the province’s health system response to COVID-19 generally, and specifically into the response to the Omicron wave. The 10 interviews averaged 65 minutes in length and included key informants from both independent PC and central health system organizations. The Interpretive Description technique was used to analyze the interview transcripts and so identify practical enactments of pro-integration culture, relationship work for integration, and integration-supportive communication. Results: Key ways of enacting a pro-integration culture included: sending signals with staffing choices; making personal expressions of commitment to the values of integration; and ensuring communal commitments to those same values. Doing integration relationships involved: following through on rhetorical commitments; and persevering at proving oneself in the eyes of counterparts from other organizations. Finally, communicating in ways that supported integration involved: bidirectional communication that included listening as well as talking; the elimination of intermediaries to close the social and knowledge distance between PC and health system stakeholders; and actuating a mix of formal and informal communication channels rather than relying on purely formal committee work or informal contacts. Conclusions: Practical steps towards achieving meso-level organization involve making high-level approaches to culture, relationships, and communication real in everyday behaviour. Efforts to integrate PC into a broader pandemic response in Alberta Canada provide a window on these specific change management implementation behaviours and so how to ‘do’ integration. Doing pro-integration culture involves sending signals through staffing choices, personal, and group commitments. Doing integration relationships involves following through on commitments and persevering. Doing integration communications involves listening as well as talking, closing the distance between stakeholders, and taking advantage of formal and informal channels. Stakeholders seeking to integrate at the meso-level may wish to consider these practical action
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".