Terapia ocupacional en mujeres con cáncer de mama, una mirada desde el modelo canadiense del desempeño ocupacional.
Bibliographic record
Abstract
Objective: The goal is to determine how the practice of Occupational Therapy, it becomes necessary in complex life situations such as breast cancer.\nMethods: The qualitative methodology was chosen, with a biographical approach, since the in-depth interview was used as a tool to obtain the data, then the information was codified through the Canadian Model of Occupational Performance in order to know systematically how to participate in the various Components emanated from this model. The analysis is made comparing the experience described in contrast with the rights approach. \nThe sample was delimited to four women with more than five years of evolution after surgical treatment. They were asked to comply with this time criterion because the treatment of hormone therapy, as it is indicated in the literature, is used 5 consecutive years of drugs, among which the most common is Tamoxifen.\nResults: These indicate that the experience of cancer breaks into the daily life of women and that affects the occupational performance, given that her way of relating to the family and other people around her is modified, rebuilt and even dissolve established relationships.\nConclusion: Through this study, it is possible to strengthen the intervention from Occupational Therapy in women with breast cancer, where practice, with a rights approach, allows validating and visualizing the inclusion of mechanisms associated with palliative care focused on maintaining or improving the quality of life of the person named cancer survivor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".