Bibliographic record
Abstract
Pooling resources, knowledge and technologies is a necessity in the health sector, both private and public. Many hospitals do so through alliances with compatible establishments, which have been studied from the organizational perspective for many years. However, many alliances are reported to fail, and the conditions which could foster their success are still not well known. The aim of this exploratory study was to identify the administrative and governance structures of hospital alliances associated with reported positive outcomes. A questionnaire was mailed to a list of hospital administrators and directors from Germany, Switzerland, Austria and Canada. Respondents were required to fill out a series of fixed alternative questions as well as some open-ended items which dealt with their perception of and experiences with, inter-hospital alliances. Administrative and governance practices were ascertained and correlated with reported outcomes. Descriptive analysis and correlations were computed using IBM SPSS statistics software. Management practices pertaining to initiation, formalization, steering and operations of alliances were correlated with financial, treatment and corporate outcomes of the alliances. Characteristics significantly linked to perceived positive alliance outcomes include: clearly defined targets and their monitoring, governance by executive management and involving the board of directors, rather formal coordination mechanisms, a project champion and a written contract including conflict resolution mechanisms. Selected structures, processes and governance practices of hospital alliances are correlated with success and therefore worth taking into account when crafting an alliance. These conclusions are derived from a multinational study and therefore could be applicable across different systems of health care.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".