Review: Professorial roles: a study of the professorial populations within nursing and midwifery, social work and allied health professions
Bibliographic record
Abstract
In educational and other research, very little is known about the career aspirations, decisions and pathways of university academics, particularly in nursing, midwifery and social work. Researchers have been far better at considering careers in others sectors than their own. This paper provides a valuable and refreshing contribution, filling a gap in the literature. Using mixed qualitative and quantitative methods to investigate the experiences and opinions and nurses, midwifes and social workers it highlights, in particular, support networks as a critical factor in their professorial success and career development. Notably the study illustrates that professors from these disciplines often suffer from a lack of institutional support, feel isolated and find it difficult to balance the different roles of health professional and academic. It also shows how they also experience pressures associated with the emerging ‘audit/performance culture’ in academia which quantifies research inputs (such as grant money) and research outputs (such as publications) and rewards those who perform best in terms of numbers. The study clearly highlights the need for educational institutions to be sensitive to the particular needs and experiences of these types of academics, and to tailor policies and procedures to their specific situations. A nursing professor, for example, will experience different pressures and have different needs, in comparison, for example, to a physicist or social scientist, yet currently they will often be judged on the same grounds, and according to the same criteria. As we move forward, it is my view that we need new ways to assess clinical work and contributions. After all, in an academic era where knowledge translation has become the new buzzword and to some extent a quality yardstick, and where others are calling for ‘public science’ and ‘public social science’ to deal directly with pressing health and social issues from the global to the local scale, we cannot expect academics to change and act unless they are assessed and supported in other ways. Beyond the specific issues this paper deals with, it speaks to this wider agenda and debate.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".