Lack of Needs Assessment in Cancer Survivorship Care and Rehabilitation in Hospitals and Primary Care Settings
Bibliographic record
Abstract
BACKGROUND: Formalized and systematic assessment of survivorship care and rehabilitation needs is prerequisite for ensuring cancer patients sufficient help and support through their cancer trajectory. Patients are often uncertain as to how to express and address their survivorship care and rehabilitation needs, and little is known about specific, unmet needs and the plans necessary to meet them. There is a call for both ensuring survivorship care and rehabilitation for cancer patients in need and further for documenting the specific needs related to the cancer disease and its treatment. Thus the aim of this study was to describe specific survivorship care and rehabilitation needs and plans as stated by patients with cancer at hospitals when diagnosed and when primary care survivorship care and rehabilitation begins. METHODS: Needs assessment forms from cancer patients at two hospitals and two primary care settings were analyzed. The forms included stated needs and survivorship care and rehabilitation plans. All data were categorized using the International Classification of Functioning, Disability and Health (ICF). RESULTS: Eighty-nine patients at hospitals and 99 in primary care, stated their needs. Around 50% of the patients completed a survivorship care and rehabilitation plan. In total, 666 (mean 7.5) needs were stated by hospital patients and 836 (mean 8.0) by those in primary care. The needs stated were primarily within the ICF component "body functions and structure", and the most frequent needs were (hospitals/primary care) fatigue (57%/67%), reduced muscle strength (55%/67%) and being worried (37%/36%). CONCLUSIONS: The results underpin an urgent need for a systematic procedure to assess needs in clinical practice where cancer patients are being left without survivorship care and rehabilitation needs assessment. Gaining knowledge on needs assessment and the detailed description of needs and plans can facilitate targeted interventions. The findings indicate an urgent need to change the practice culture to be systematic in addressing and identifying survivorship care needs among patients with cancer. Further the findings call for considering the development of a new needs assessment form with involvement of both patients and healthcare professionals.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".