The Perceived Information in Obtained From the Informed Consent in Iranian Patients With Cancer in Clinical Studies
Bibliographic record
Abstract
OBJECTIVE: One of the basic issues in clinical studies is to receive the informed consent; that is to say, all the activities applied in patient's involvement in the information, decision-making, ability and volunteering in diagnosis, cure and care. In as much as most cancer patients require information about their individual needs, the present study is conducted to determine the perceived information from the informed consent of clinical studies in cancer patients. METHODS: This is a descriptive study. Fifty cancer patients hospitalized for participating in the clinical study was chosen according to the convenience sampling. Tools used in this research included the questionnaire (individual and social features) and the check list about patient's right and cancer patient's information before and after receiving informed consent in clinical studies (10 items on a Likert rating scale). To validate the study, content and formal validation was used. Data in this research were analyzed using descriptive statistics (frequency, mean and standard deviation) and the software of SPSS 16. RESULT: In general, the mean of the scores obtained from cancer patients' perceived information before completing the informed consent of the clinical studies was 14 ± 3.5 and after consent of the clinical studies was 16 ± 2.4. The cancer patients' perceived information before and after consent of the clinical studies was weak. CONCLUSIONS: Based on the findings of the present study, the rate of the information the cancer patients received, before completing the informed consent form, was low, but after completing the informed consent form this rate was again low. Therefore, conducting similar and wider studies is recommended to unveil the factors affecting perceiving information and how to promote the quality of the informed consent in other hospitals in Iran.
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".