Bibliographic record
Abstract
I who are diagnosed with cancer experience more than a physical impact. There are emotional, psychosocial, spiritual, and practical consequences as well. While some individuals manage to cope successfully with the many changes, others experience on-going difficulty and emotional distress. If unchecked, this emotional distress can escalate and eventually interfere with problem-solving, adherence to treatment, and overall adjustment. The wide range of variation in coping behaviors and adaptation stages make selecting the correct intervention challenging. Clearly, astute assessment is required as the basis for developing a tailored approach to interventions. If the supportive care needs of those living with cancer are to be met appropriately, intentional approaches are needed in busy clinical settings to identify, assess, and manage the distress. Without concrete efforts, the supportive care needs may easily be overlooked and the predominant focus of the health care team will remain on tumor assessment and treatment. Person-centered or whole person care will not be the focus of the team’s interactions. This presentation will outline the supportive care needs of cancer patients and summarize the evidence concerning the level of unmet need in cancer populations. Programmatic approaches to identify patient concerns and distress related to their supportive care needs, assess at a deeper level when necessary, and intervene based on relevant evidence will be discussed. Cancer centre need to be thinking about adopting programmatic approaches for this area of care as health service accreditation standards cite attention to supportive care needs of patients as a requirement within quality patient care. Margaret I. Fitch, J Nurs Care 2013, 2:3 http://dx.doi.org/10.4172/2167-1168.S1.002
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".