Bridge to home : how to improve patients’ transition from acute care to back to normal life in oncology
Bibliographic record
Abstract
Context: An increasing number of patients are living with cancer in a chronic way, which requires followup adapted to the context and the risks of recurrence. However, the transition to life after acute cancer treatment is not well prepared during the treatment: patient doesn’t know how to act and use the communitarian resources to go through this transition and general practitioner (GP) don’t receive the relevant information to be able to follow-up adequately those patients. Objective: To describe what a regional health center in Québec Canada has put in place to better prepare their patients for this transition. Study design and analysis: A before/after study has been conducted. A sample of patients having done a transition during the last 6 months were surveyed as GPs to better understand the needs and how to answer to their issues. After collecting the needs, patients partners and professionals decided to implemented an intervention to respond to those needs. After the implementation, patients who have done the transition were surveyed to assess how the interventions responded do their needs. Setting: the intervention was settle in the cancerology department of a regional health and social services authority in Québec, Canada. Population studied: a sample of patients before and after the intervention implementation and a sample of GP before the implementation. Intervention/instrument: The intervention consisted on : 1) a consultation with an oncology nurse to assess the patients’ needs; 2) fill out a transfer note which summarize the patients conditions and follow-up sent to the patient’s GP; 3) developing patient education material. The design to the intervention was coconstructed by a committee composed of half patients and half professionals (from oncology centre and primary care settings). Results: Prior to the implementation of the intervention, 21 patients were interviewed (100% response rate). They did not receive any information that would allow them to better anticipate the different symptoms and emotions they will have to face. In addition, 86% of the GPs did not receive relevant information to enable them to be well prepared to offer follow-up to their patients. After implementation, 25 patients responded (85% response rate) and appreciated receiving a standardised note in the month following their transition.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".