The influence of gender, ethnicity, class, race, the women’s and labour movements on the development of nursing in Sri Lanka
Bibliographic record
Abstract
The paper reveals that historically various socio-political factors, including gender, class, ethnicity, race, waves of colonization, decolonization, the civil and ethnic wars, the women's and labour movements, have influenced the development of nursing in Sri Lanka. However, literature presenting the development of nursing in Sri Lanka is sparse. All relevant journals and books published in the English and Sinhalese languages on nursing in Sri Lanka between the years 1878-2011 were examined. Because there are no nursing journals currently produced in Sri Lanka, CINAHL and Medline databases were accessed and relevant literature published in the English language on Sri Lanka was examined. Government, nurses' union and association reports, other unpublished reports and websites such as Google were also searched to access information related to the influence of gender, race, class, ethnicity, women's and labour movements in Sri Lanka. Poor pay, shortages of resources, failure in recruitment and retention and limited opportunity for career progression have acted as deterrents to persons entering and remaining in the nursing profession. Being non-British was a key issue in terms of race. Further, the shift from a colonized state to a welfare state resulted in a class shift from upper middle class to middle and lower class persons entering into nursing. Although there is a paucity of information available in the nursing literature, this analysis offers an intriguing insight into an angle that may be used to examine the influence of gender, ethnicity, class, race and the women's and labour movements in other contextual situations.
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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 | 0.002 | 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 | 0.000 |
| 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".