Post-basic education training, age and gender of healthcare workers for elderly patients in Germany and Canada: A comparison
Bibliographic record
Abstract
BACKGROUND: This study compares Canadian and German healthcare workers employment status and wages based on age, gender, and educational training. The German and Canadian healthcare systems are rarely compared, that if such a comparison between the two countries is available, can provide an insight of health workers employment status and how that might affect the wellbeing of elderly. OBJECTIVES: The study investigates the relation of age, gender and post-basic training with annual employment and income of health care workers in Canada and Germany. METHODS: Secondary data on age, gender, post-basic education training, employment status and average monthly wages/salary was obtained from the German Socioeconomic Panel and the Canadian Survey of Labor and Income Dynamics data. The German dataset comprised 571 healthcare workers, including 219 nurses, 231 elder carers and 121 care assistants. The Canadian dataset comprised 2,580 healthcare workers, including 947 nurses, 493 elder carers and 1,140 care assistants. RESULTS: Primarily, there was a strong relationship between post-basic training and wages for both the Canadian and German samples among elder carers and care assistants. Older healthcare workers (46 years old and above) were generally employed, and age had no predictive power on annual labor earnings. This difference in age was significant among the Canadian than the German nurses. Post-basic training had a significant relation with annual earnings and monthly wages in both the Canadian and German samples. CONCLUSIONS: This study suggests that the socio-demographics of healthcare workers as age, gender and advanced training are associated with their employment opportunities, financial rewards, and better career opportunities. The relationship between demographic variables helps understand the relation of healthcare workers employment dispositions and how it might improve the quality of life of older people in nursing homes, hospitals and elderly private homes in Canada and Germany.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".