Bibliographic record
Abstract
Leading Health Care Organizations Sue Dopson and Annabelle Mark (eds) Palgrave MacmiUan: Hampshire and New York 2003, HB 266 pp, AUD 95.00, ISBN 1-4039-0270-4I confess to having been ill-disposed towards this volume a few sentences into the preface. Here Keith Grint justified its publication by arguing that even though there have been over 12,000 books written about leadership, we do not really know if it makes any difference or how. This seemed at once indefensible (if true, a monstrous waste of scholarly and other effort that should not continue) and patently untrue (as the papers in the volume went on to demonstrate). Suppressing the urge to read no further, I went on to find this collection a good demonstration of the fact that we do know quite a lot about leading health care organisations.This is the third volume arising from the Third International Conference on Organisational Behaviour and health Care held at the Said Business School, University of Oxford, in 2002. The conference theme was leadership in health care and the 15 papers selected for this volume are organised into three sections reflecting areas of focus at the conference:- the nature of leadership in health care;- leading health care organisations; and- taking health care forward in the 21st Century.The section devoted to the nature of leadership in health care has five chapters covering medical leadership in primary health care networks (UK), 'cognitive sculpting' in nursing leadership development (UK), leading multi-disciplinary teams (USA), clinical governance through effective leadership (UK) and an ethnographic study of clinical leadership in an ICU (UK). One of the more interesting is Sheaff and colleagues' analysis of the tensions between managerial and professional leadership in UK primary health care networks. Such analyses serve to remind us what an anomalous industry primary care health is in some countries (the UK and Australia amongst them): one where private business professionals are providing, for profit, a publicly funded public good. Not surprisingly this throws up major challenges for management, quality and accountability. Wells and colleagues' paper on how to empower workers of differing social and professional standing in multi-disciplinary teams offers some important lessons for effective leadership in this greatly expanded facet of contemporary primary health care. And Carmel's ethnography of leadership in an ICU, which highlights the importance of leaders being able to manage uncertainty, has relevance well beyond the bounds of this narrow clinical setting.The section devoted to leading health care organisations has four chapters that address knowledge management and communities of practice (UK), inter-agency multi-disciplinary teams (USA), the conceptual and practical validity of contemporary constructions of 'organisational leadership' (Australia), and the application of Programme Budgeting and Marginal Analysis as a tool to support resource distribution decision-making (Canada). Bate and Robert's knowledge management paper picks up on the National Health Service's (NHS) explicit interest in drawing on private sector know-how to inform knowledge management in the NHS. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".