Uncertainty in Medical and Dental Students of Pakistan Regarding Their Future Career and Its Major Causes: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the extent of confidence in medical and dental students regarding their careers after being enrolled in their germane institutes and to identify the magnitude of uncertainty in medical and dental students about their careers and the important causes of this uncertainty. METHODS: This study was conducted among enrolled students in different medical and dental colleges and universities of Pakistan, including the public and private sectors, from March 1 to March 15, 2023. The level of confidence in their career was evaluated using a close-ended questionnaire of a three-point Likert scale developed and tested by the investigators and approved by the Ethical Review Board (ERB) along with the synopsis. Data were managed and analyzed using Microsoft Excel 19 (Microsoft Corporation, USA) and IBM SPSS Statistics for Windows, version 27 (released 2020; IBM Corp., Armonk, New York, United States). RESULTS: This study included 1,126 students from public and private medical and dental institutes. The majority of participants 965(85.7%) were satisfied with their chosen profession, and 1,042 (92.5%) students believed they could make a positive contribution to society. Out of the total participants, 246 (21.8%) students showed their intention of changing careers if provided with a comparable/alternative opportunity. A very small proportion, 154 (13.7%), were dissatisfied with their current clinical training and studies. The study also revealed that extra and unjustified academic pressure from institutions is the leading cause of uncertainty among students. Moreover, the lack of psychological support and counseling provided during the academic years adds to the magnitude of uncertainty. CONCLUSION: In Pakistan, a staggering number of medical and dental students are unsure of their future careers and career prospects. The main causes of this uncertainty are the extra, unwarranted academic pressure that institutions place on students and the dearth of psychological support and counseling offered during the course of studies. This study not only highlights the prevailing uncertainty among medical students but also identifies the causes behind it. Addressing these causes can alleviate the prevailing uncertainty and bring about satisfactory and productive academic achievements without suffering from worries about the future.
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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 | 0.001 |
| 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".