Tracking the Workforce 2020-2030: Making the Case for a Cancer Workforce Registry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing literature has described the projected increase in cancer incidence and the associated deficiencies in the cancer workforce. However, there is currently a lack of research into the necessary policy and planning steps that can be taken to mitigate this issue. Herein, we review current literature in this space and highlight the importance of implementing oncology workforce registries. We propose the establishment of cancer workforce registries using the WHO Minimum Data Set for Health Workforce Registry by adapting the data set to suit the multidisciplinary nature of the cancer workforce. The cancer workforce registry will track the trends of the workforce, so that evidence can drive decisions at the policy level. The oncology community needs to develop and optimize methods to collect information for these registries. National cancer societies are likely to continue to lead such efforts, but ministries of health, licensing bodies, and academic institutions should contribute and collaborate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it