A Comparative Study of Data Collection Methods in the Process of Nursing: Detection of Chemotherapy Side Effects Using a Self-Reporting Questionnaire
Bibliographic record
Abstract
Toxicity of chemotherapy is a factor that most negatively aff ects the quality of life of cancer patients.Monitoring of side eff ects and adverse eff ects may be subject to errors due to various factors such as the lack of privacy during data collection, shame on the part of the patient to talk about some issues, lack of recognition of symptoms and/or unawareness of side eff ects of treatments, and/or inappropriate reference model of data collection.In order to assist caregivers in proper data collection, a 'self-reporting questionnaire' was designed.Th e questionnaire was developed using validated scales such as the Common Terminology Criteria for Adverse Event, Edmonton Symptom Assessment Scale and Douleur Neuropathique en 4 Questions.Th e survey involved the population of patients scheduled for chemotherapy in Day Hospital at the Campus Bio-Medico University Hospital, Rome, between June and July 2015.During the period of observation, 367 patients were admitted to Day Hospital, 57.5% of women and 38.4% of men, average age 64 years, for a total of 622 accesses; of these, only 173 were interviewed by the nursing staff in relation to side eff ects and toxicity.During the trial, 381 patients were involved, of which 60.1% of women (p=0.8) and 38.3% of men (p=0.9),average age 63 years (p=0.9),for a total of 611 accesses and 498 self-reporting questionnaires administered.At the end of the trial period, in order to evaluate usability, an evaluation questionnaire was given to medical personnel, including fi ve doctors and six nurses, to consider possible amendments to the instrument and its perceived eff ectiveness.Comparative analysis of data collected during the observation period and the trial showed how the use of the self-reporting questionnaire allowed for detection of side eff ects of chemotherapy earlier and in a more detailed way than relying only on medical examination and unstructured interview by nursing staff .It also enabled reaching a larger number of users.In conclusion, the use of self-reporting systems, together with the work and clinical judgment of the expert, can contribute to improvement in the patient quality of life, corroborating nurse interviews through a precise and systematic data collection process that reduces the amount of interpretation of symptoms by the patient and the caregiver, while providing them with precise instructions on what to report and how to report it.Th e signifi cant and rapid spread of computers, tablets and smartphones allows for speculating on further use and implementation of this system through its computerized application.
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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 | 0.084 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".