A Comparative Study of Data Collection Methods in the Process of Nursing: Detection of Chemotherapy Side Effects Using a Self-Reporting Questionnaire
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».