‘Trying to patch a broken system’: Exploring institutional work among care professions for interprofessional collaboration
Bibliographic record
Abstract
Abstract There is a growing interest in understanding when and why interprofessional collaborations are well functioning, especially within healthcare systems. However, more knowledge is needed about how professionals affect and contribute to these collaborations when they engage in them. To address this shortcoming, this study aims to contribute to professional and organizational studies of interprofessional collaboration by providing novel insights into how professionals engage in and contribute to interprofessional collaborations. It builds on a theoretical perspective of examining professionals’ everyday collaboration practices through the interplay between temporal-oriented agency and institutional work. It applies this perspective to a case study of interprofessional collaboration between personal workers (PWs), nurses, and therapists in the home care sector in Denmark. Overall, the findings show that the professionals engaged in and contributed to the interprofessional collaboration by ‘trying to patch a broken system’. All three professional groups did this primarily by ‘adopting new practices to deal with inept institutionalized practices’ to maintain collaboration. Additionally, some PWs ‘failed to enact institutionalized practices’ to disrupt the collaboration, and some nurses and therapists ‘invented and established mechanisms’ to create new arrangements for the collaboration. Based on the findings, the study demonstrates that certain dimensions of agency are associated with certain types of institutional work. Furthermore, the study suggests that the interplay between agency and institutional work varies between professional groups, influenced by their relative autonomy.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".