Retention and Task Shifting in Human Resources for Health through Data Mining
Bibliographic record
Abstract
Human resources for health (HRH) are the backbone of the healthcare system, but a shortage of medical manpower and the misdistribution of human resources are critical problems in the rural areas of many countries till 2017. The shortage of medical manpower is a big issue between 2004 and 2013. Data mining of bibliometrics is a good tool to find the solutions for shortage of medical manpower. By analyzing 118,092 citations in 2,000 articles published in the SSCI and SCI databases addressing HRH from 2004 to 2013, we plotted the networks among authors in the field. We combine quantitative bibliometrics and a qualitative literature review to determine the important articles and to realize the relationships between important topics in this field. We find that retention and task shifting are the hot topics in HRH field between 2004 and 2013, and find out the solutions for these issues through literature review in later papers. The solution to the HRH shortage is to determine the motivations of health workers and to provide incentives to maintain their retention. Task shifting is another solution to the HRH crisis.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".