Common Errors In Proposal Writing: A Cross Sectional Study Among Post Graduate Medical Trainees At Peshawar.
Bibliographic record
Abstract
BACKGROUND: Proposal writing before starting research study is the key component of the any research project and quality of the research depends upon how the proposal was designed and planned. Objectives of this study was to determine the frequency of most common errors in proposal writing by post graduate medical residents of College of physician and surgery of Pakistan (CPSP) at Peshawar. METHODS: A cross sectional study was carried at Khyber College of Dentistry (KCD) Peshawar from August 2017 to May 2018. We conducted the reviewed of Form "S" of 43 proposals through convenience sampling. Each Form S consists of 34 questions. All the questions were dichotomous which were presented in the form of frequency and percentages. Data were analysed by SPSS-22. RESULTS: Out of 43 proposal, the result shows that 53.5% (n=23) of the candidates have not explained the introduction in their own words while suitable statistical tests were not mentioned in more than half of the 67.4% (n=29) proposal. References were not written in Vancouver style 51.2% (n=22) as well as hypothesis was not applicable in 62.8% (n=27) of the studies. However only 39.5% (n=60.5) of the trainees phrased the hypothesis properly. CONCLUSIONS: Majority of the candidates were unable to write the proposal according to the recommended guidelines. Application of the appropriate statistical measures was found as a challenge for the candidates. Similarly, objectives were not clearly defined in terms of SMART concept.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".