Nutritional care in advanced cancer, the experiences of patients, families, and health care providers
Bibliographic record
Abstract
Anorexia and cachexia are prevalent problems in palliative cancer patients. To date, however, the majority of research related to these issues has been biomedical in nature. While this line of inquiry has produced important information regarding the pathophysiology and clinical management of cancer malnutrition, little is known about the experience of nutritional care from the perspective of patients, families, and health care providers. The minimal literature that exists on this topic suggests that these key stakeholders may hold divergent views about what constitutes appropriate nutritional care in the face of advanced disease, and that this divergence results in conflict among and between these parties. However, the concepts relevant to this dynamic are poorly understood and conceptually underdeveloped. Therefore, the grounded theory approach to data collection and analysis was used to develop a beginning substantive theory aimed at uncovering the social processes inherent in patient, family and health care provider interaction around the issue of nutritional care. Data were collected, by means of the conversational interview, participant observation, and chart review, from 13 cancer patients receiving in-hospital palliative care, 13 family members, 11 health care providers delivering in-hospital palliative care, and 10 bereaved family members. The basic psychosocial problem uncovered in the data was family members' needs to balance the means and goals of nutritional care while simultaneou ly meeting their own needs and goals related to the provision of this care. The unifying theme of "doing what's best" integrated the major categories into the key analytic model in this study. "Doing what's best" represents a continuum of behaviors and strategies, and includes the sub-processes of "fighting back: it's best to eat"; "pseudo-surrendering: holding on while letting go"; and "letting nature take its course: it's best not to eat." The extent to which family members embrace a particular sub-process and/or might move back and forth among them is a complex process involving many factors related to the patient, family member, health care provider, and the context in which the interaction about nutritional care takes place.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".