Using strategic communities to foster inter-organizational collaboration
Bibliographic record
Abstract
Purpose The purpose of this paper is: to report on an experiment in building up inter-organizational collaboration between healthcare organizations; and to identify how structure and some of the components of the strategic community (SC) approach to organizational change can have a long-term impact on inter-organizational collaboration. Design/methodology/approach This paper resulted from participative action-research held from 2007 to 2013. A systematic collection of data (field notes, 746 hours of observations, proceedings, 186 interviews, journals, focus groups, discussion forums) was conducted in the various cycles of the action-research. Findings Adapted to the healthcare sector, the SC has taken the form of a temporary inter-organizational collaboration structure composed of health professionals, first-level managers, general practitioners, specialized doctors, and non-profit organization representatives. The SC approach appeared to be an efficient strategy for taking action. Practical implications The SC approach appeared to be appropriate for cases where the inter-organizational collaboration had clearly declined, where several other attempts had failed, and where the care trajectory involved vulnerable clients who had to travel between different service points for the required care. Originality/value This study illustrates how SC helps to significantly improve inter-organizational collaboration in the healthcare sector. It likewise acknowledges the relevance of Thomson and Perry’s (2006) work in analyzing and emphasizing the dimensions required to ensure successful inter-organizational collaboration.
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 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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".