Health Care Service Quality and Availability of Skilled Health Workforce: A Panel Data Modelling of the UK, USA and Israel
Bibliographic record
Abstract
In the recent decade, as the number of new health related issues are on the rise, more qualified medical specialists are needed, who can advocate the importance of adopting innovative means of diagnosing health problems. There are many qualitative studies that has emphasised that there is two way relationships between health care service quality and availability of skilled health workforce; however, the significance of this relationship is still unclear. This study utilises the panel data modelling technique (PDMT) to examine the relationship between health care service quality and availability of skilled health workforce by drawing data from the Organisation for Economic Cooperation and Development (OECD) database. Based on the availability of data, three countries were studied in this paper and these three countries are on USA, UK and Israel. The findings from this study showed that the status quo of the health care service delivery can be improved in the USA and the UK if more nurses, irrespective of domestic or foreign trained nurses, are hired. In the context of Israel, more locally trained doctors and nurses rather than foreign trained doctors and nurses needs to be hired, as locally trained doctors are better able to communicate issues related to local public health to the patients.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".