Extending Working Life - Health Professionals Late Career Transitions, a Demographic Time Bomb for the UKs National Health Service:European Academy of Occupational Health Psychology Conference 2016
Bibliographic record
Abstract
The recently introduced increases in pension age for UK National Health Service (NHS) employees (rising to 68 years by 2028) sponsors the intuitive conclusion that this will result in people working longer and retiring later. While this is likely to be the case for a proportion of employees, there are grounds for speculating that this may not be the outcome for a large proportion of NHS workers. Our reasons for believing this are based on an analysis of NHS age demographics and employment migration patterns, using a sub-set of data from the UK Office for National Statistics (ONS)Labour Force Survey (the Annual Population Survey - APS, and the five-quarterly longitudinal 5QLFS), for the period 1993-2012. The APS produces an annual sample of 4,100-4,200 NHS employees. This analysis, commissioned by the tripartite NHS Working Longer Review Group, revealed an established trend of a large proportion of employees leaving the NHS significantly before their pension date, a process that begins from about age 50 years onwards. A high proportion of this group continue in employment outside the NHS. It also revealed that NHS employees have a relatively high, and rising, mean age (currently 43 years), around four years higher than the private sector. The mean for some professions is appreciably higher than this (e.g. 48 years for paramedics). If the established pattern of early withdrawal continues, in the context of a rising mean age, there is a risk that the NHS will experience significant labour shortages within a decade. The paper discusses what is known about the push and pull influences that are associated with early withdrawal, the post-NHS employment destinations of those who leave, and the scope for employer action to increase rates of retention of older health professionals.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".