Development and validation of an individualized survivorship care plan (ISCP) for women with endometrial cancer during the transition of the end of active treatment to the cancer survivorship
Bibliographic record
Abstract
Many cancer survivors finish their treatment without knowing the associated health risks and few are prepared to handle their health needs in the survivorship phase. Moreover, practical guides for follow- up care are not available and survivors' psychological and social needs often go unassessed. In this article, we propose the development and implementation of an individualized follow-up care plan (IFCP) after active treatment for women with endometrial cancers (WEC) to meet their needs for information and to facilitate the transition to the survivorship phase. BACKGROUND: The after-treatment phase is a distinct phase that is still neglected in the oncological continuum of care. It is the transition between two phases in the care trajectory-treatment and survivorship-that gives rise to many challenges for survivors, care providers and the healthcare system alike. RESEARCH GOAL: Aiming to facilitate the transition between the end of active treatment and the cancer survivorship phases, we pursued two objectives: 1) Develop an individualized follow-up care plan (IFCP) based on both the literature and the perspective of WEC, healthcare professionals involved with the target clientele and oncology outreach managers, and 2) Have this IFCP validated by an interdisciplinary team. METHODOLOGY: For the first objective, WEC-related needs at the end of active treatment (immediate end, three months and six months), as well as the perceptions of health professionals and oncology outreach managers were gathered by interview and group discussion on the benefit of an IFCP, its content and desired format. A content analysis of the interview data was performed using the Miles and Huberman approach (2003). For the second objective, an iterative consultation process with health professionals allowed for validation by consensus. These two objectives are the first qualitative phase of a mixed-methods sequential exploratory design that will make the development of an IFCP possible. In the second phase, we conducted a feasibility study of the implementation of the IFCP during the end of active treatment transition to cancer survivorship transition. This will be the subject of a second article. RESULTS: The interviews (n=47) revealed WECs' lack of preparation for the transition from the end of active treatment to the cancer survivorship. The following needs were specified: information (80%), emotional support, particularly to overcome their fear of recurrence (75%), the management of physical symptoms (45%), and support for adapting to change (45%). The data gathered from healthcare professionals and outreach managers support the utility of an IFCP in meeting these needs. The iterative validation process by the interdisciplinary team made consensus on the format and content possible. The final version of the IFCP is seen as a tool for information and communication in the survivorship phase. Some obstacles to its transfer to clinical practice are reported. CONCLUSION: This study presents the entire process that led to the development of an IFCP that integrates both the needs of endometrial cancer survivors and the opinions of healthcare professionals and the oncology outreach managers organizing this care. Indications on how the IFCP could be implemented within this organization are also formulated.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".