Bibliographic record
Abstract
Healthcare integration projects typically involve significant organizational change, with the intention of providing improved patient services and outcomes through the integration of healthcare services. Some of the management needs of healthcare integration arguably go past the traditional domain of project management, leading practitioners in these projects to use change management in combination with project management. The focus of this article is on the ways that project managers, responsible for merging and integrating healthcare services, have used project management and change management approaches in combination when delivering these projects. The article involves an inductive analysis of data from the integration of 10 healthcare networks. The aim of this article is to contribute to the growing stream of project management literature that explores the blending of both project management and change management. Analysis of these healthcare integration projects led to five key themes, which can be summarized as: (1) traditional project management only partly aligns to the needs of healthcare integration projects; (2) benefits were found in combining project management with change management; (3) change management was particularly beneficial if used early in the project life cycle; (4) the life cycles of these two disciplines did not align, causing complications in practice, and (5) practitioners used an intuitive and improvisational approach to combining the disciplines.
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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".