Estrategia educativa en población geriátrica con diagnóstico oncológico del Policlínico “Dr. Mario Muñoz Monroy”
Bibliographic record
Abstract
Introduction: In Cuba there is a marked increase in the population over 60 years old, while cancer is the leading cause of death in this age group, representing 73% of all deaths from this disease. Objective: Design an educational strategy addressed to caregivers and elderly patients with oncological diagnosis from Dr. Mario Munoz Monroy Polyclinic of Guanabo. Methods: A prospective and longitudinal study was designed with the application of a questionnaire to patients and caregivers of the elderly population diagnosed with neoplasia, belonging to the health area Guanabo, Habana del Este municipality, Havana province. An educational strategy was developed and it was validated during the first and last quarters of 2018. Results: Most of the patients were aged between 60-69 years, the male gender prevailed and the oncological locations lung, breast and colon encompassed almost half of the patients, 38 patients had more than one year of being diagnosed. Daughters and wives were the most represented caregivers. The onset of pain without relief and feeding were the biggest concerns for patients and caregivers. An educational strategy was designed and applied with talks, newsletters and consultations, which when evaluating its impact turned out to be 82.7% and 90.5% in the first quarter and the last quarter of 2018, respectively. Conclusions: The designed educational strategy managed to expand the knowledge of caregivers and patients on the appropriate way to administer food and medicines, their adverse effects and their management, and care at home. In addition, its validity over time could be demonstrated. Keywords: Educational strategy; cancer; population ageing; caregivers; pharmacists.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".