Preparedness for colorectal cancer surgery and recovery through a person-centred information and communication intervention – A quasi-experimental longitudinal design
Bibliographic record
Abstract
To meet patients' information and communication needs over time in order to improve their recovery is particularly challenging for patients undergoing cancer surgery. The aim of the study was to evaluate whether an intervention with a person-centred approach to information and communication for patients diagnosed with colorectal cancer undergoing surgery can improve the patients' preparedness for surgery, discharge and recovery during six months following diagnosis and initial treatment. The intervention components involving a novel written interactive patient education material and person-centred communication was based on critical analysis of conventional information and communication for these patients. During 2014-2016, 488 consecutive patients undergoing elective surgery for colorectal cancer were enrolled in a quasi-experimental longitudinal study. In three hospitals, first a conventional care group (n = 250) was recruited, then the intervention was introduced, and finally the intervention group was recruited (n = 238). Patients' trajectories of preparedness for surgery and recovery (Preparedness for Colorectal Cancer Surgery Questionnaire-PCSQ) health related quality of life (EORTC QLQ-C30) and distress (NCCS Distress Thermometer) were evaluated based on self-reported data at five time points, from pre-surgery to 6 months. Length of hospital stay and patients' behavior in seeking health care pre- and post-surgery were extracted from patient records. Longitudinal structural equation models were used to test the hypothesized effects over time. Statistically significant positive effects were detected for two of the four PCSQ domains (patients searching for and making use of information, and making sense of the recovery) and for the role functioning domain of the EORTC QLQ-C30. Patients in the intervention group were also more likely to contact their assigned cancer "contact nurse" (a.k.a. nurse navigator) instead of contacting a nurse on duty at the ward or visiting the emergency department. In conclusion, the overall hypothesis was not confirmed. Further research is recommended on written and oral support tools to facilitate person-centred communication.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".