Governance and coordination in health care: organic processes and structural capacity
Bibliographic record
Abstract
Purpose The purpose of this paper is to articulate cutting-edge conceptions of the relationship between local processes in the here-and-now, and the broader influences on those processes, that are both organic and overtly designed, and to discern the implications of this relationship for future research, policy and practice. Design/methodology/approach A focused and structured approach was taken to give effect to this purpose by reviewing the chosen articles in this collection, which from the 2018 Organizational Behavior in Health Care conference papers. Findings Research in coordination within and across health care boundaries increasingly recognizes: the multilevel influences on human action and interaction in health care delivery; the challenge of balancing individual or local agency with overt interventions; the everchanging the local circumstances of healthcare delivery; and the need to foster reflexivity, that is, self-improvement capacity, in healthcare organizations. Research limitations/implications Interventions to improve care coordination must be grounded in the reality of changing local circumstances and incentives for action from the broader environment. Originality/value This paper articulates the implied tension in health care delivery between individual and local agency, and imposed structures that may contradict, but are at the same time necessary, to foster such agency.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".