Needs Assessment Study in a Dementia Colombian Population
Bibliographic record
Abstract
Abstract Background Dementia is a public health problem. In Colombia, there is no policy for dementia patients or their caregivers. Patients with dementia have unidentified needs and this lack of knowledge has contributed that a formal policy has not been raised. The recognition and identification of the needs facilitate the planning, provision, and evaluation of health services, and constitutes the first stage for the implementation of services or care plans. If the Colombian State wants to formulate policies that protect the population with dementia it should conduct studies that contemplate all dimensions and guarantee the social inclusion and equity of the affected population. A needs assessment study is required that includes the identification and analysis of physical, social, patrimonial, legal, access and psychological needs of patients with early stages of dementia and their caregivers. Objective To identify and analyze the needs of Colombian patients with mild dementia and their relatives with the purpose of contributing to the design of the health policy strategy for dementia, from a perspective of social justice and equity. Method Exploratory sequential mixed methods design. We will include Colombian adults with a mild dementia diagnosis, their caregivers and health professionals. We will employ a structured interview, an assessment of quality of life, psychiatric and medical comorbidities and we will employ case studies and focus groups to identify categories and interactions of the study problem. In the second stage, we propose to employ a needs assessment methodology and a mixed method using triangulation to evaluate the burden of known and unknown needs. Then, a theoretical analysis of inequities will be carried out stratifying by the insurance regime and city. Result The results will be analyzed in light of the theory of recognition as social justice. Conclusion We will recommend a proposal for the design of the strategy in health for this population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".