Clinical learning environment in government and private hospitals in relation to gender in Karachi, Pakistan - a comparative study
Bibliographic record
Abstract
Introduction: Background: The Medical Education has two most essential basis, sound theoretical knowledge and proficient clinical skills. Both of these factors depend on an efficient method of teaching in a proper clinical set-up. So that the medical students get most of the benefit out of it by learning and developing their clinical skills. Objective: The main objectives of this study are to determine the presence of difference in the learning environment in government and private hospitals of Karachi and to determine whether there is any biasness present in these systems on the basis of gender of students.Methods: Four renowned university hospitals were selected: two private and two government-based. 150 samples were collected from each university by a self-administered questionnaire. The analysis has been performed using SPSS 20 and the results are presented using chi square.Results: The study established that there is a striking difference between the learning environment in these clinical set-ups based on the theoretical knowledge of the students, the proper schedule of classes, hands on experience gained in the ward, new knowledge gained at the end of a wards rotation, doctors’ interaction with the students and the students’ motivation. This was represented by the 2 = 10.173 and p .00001. MG = 5.73 with a SD = 1.815 while MP = 6.76 with a SD = 1.883 (M = mean, G = Government hospital environment, P = Private hospital environment). According to the study there is no difference in treatment of the students on the basis of gender represented by the 2= 3.168 and a p is between .05 and .1.Conclusions: The private hospitals provide a sound theoretical knowledge to the students they lack the provision of optimum opportunities for the students to develop their clinical skills in comparison with government hospitals. It is however recommendable that both the systems lack any biasedness among the students on the basis of gender.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| 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 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".