Sustainable funding for nursing research in higher education
Bibliographic record
Abstract
The development of a stable and sustainable funding base for nursing research has proved an elusive goal in many countries. Laying the research foundation is increasingly recognised as critical to the sustainability of nursing's hard-won place in higher education. The recent launch of a development award scheme by the Higher Education Funding Council for England (Hefce) and the Department of Health (DoH) in England, designed to increase the amount of high quality research related to nursing and allied health professions, marks a watershed in UK research policy. The announcement of an initial £4.8 m over five years from the DoH comes with the expectation that Hefce will add its share to the fund, with an announcement following the government's Comprehensive Spending Review in late Autumn 2002. This policy reflects a reversal of the previous multidisciplinary policy within which nursing and midwifery was expected to compete with groups with a more mature research base for funding. The establishment of the DoH/Hefce fund has been accompanied by related initiatives from other parts of government and the foundation world. The PPP Foundation have so far invested £3.4 m through fellowship schemes at the doctoral and postdoctoral levels. Together such initiatives begin to break the cycle of disadvantage which has dogged the development of nursing and midwifery research in recent decades. The change of direction that policy has taken has resulted from an alignment of agendas between the professions and government, and most important of all, the availability of resources to invest and political will to do so. The announcement of government funding was based on the recommendations of a task group, which examined how high-quality research relevant to nurses and allied health professions (AHPs) could be better supported. The report drew heavily upon research to which a team of us contributed which was intended to create the evidence-base and policy justification of change. Our report noted that research in nursing, midwifery and allied health professions was significantly under-funded in relation to other comparable professions such as teaching and social work, and benchmarked poorly against government investment in countries such as the USA and Canada. We used several approaches to collecting data: questionnaire survey to academic departments, visits to institutions, case studies, bibliometric analysis, extensive interviews and consultations with key stakeholders. As well as focussing on the supply of research and researchers we also attempted to quantify the demand, including relative demand with respect to other benchmarks. Finally, an attempt was made to estimate the ‘payback’ which might result from investment, mindful of the Treasury as the ultimate audience for the findings. We argued that investment was justified on the basis that the quantum (?) of research and researchers was rising and nursing, midwifery and AHPs departments were generating increasing research income. The capacity to do research had also been increasing; over the five year period to 1999, nursing, midwifery and AHPs research staff in universities had grown in number but still represented only 3.9% of the total staff. Comparable figures for other benchmark disciplines were education 7.6% and social work/studies 13.3%. Postgraduate student numbers in nursing had also grown by 94%, amounting to 3700 in 1998–99; but again, all but 435 of these were part time. Bibliometric analysis showed a matching increase in published papers over the last 10 years, but caution was required here since the outputs for nursing and midwifery had not increased since 1995. Most worryingly, however, a high proportion of funded papers (c. 80%) revealed no funding source, implying they were self -funded. Comparisons with education, a professional area with a similar profile, revealed that a weakness in research capacity and outputs was recognised within the UK in 1998 by the creation of a special teaching and learning research fund managed for Hefce and the Economic and Social Research Council (ESRC). Nursing in the UK also compared unfavourably with investments in the USA, and more recently Canada, which has just embarked on a major capacity-building initiative via the appointment of chairs in nursing research and planned spending of $25 m over the next ten years (CPNR 2001). Whilst it may be tempting to believe that the compelling nature of the case won the day, evidence is rarely sufficient in itself to bring about change. Many other factors come into play (Rafferty, Bond and Traynor 2001). The consequence of this underfunding has been a cycle of disadvantage in which nurses have failed to benefit from the collaborative research and peer-review processes necessary to strengthen competitive capacity. We now have a unique opportunity to escape from this cycle. We need to build strong multidisciplinary research teams and strengthen our links with patient and user groups to realise the ‘payback’ for stakeholders and a sustainable future for nursing in health care and higher education.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".