Effect of PARACT (PARAmedical Interventions on Patient ACTivation) on the Cancer Care Pathway: Protocol for Implementation of the Patient Activation Measure-13 Item (PAM-13) Version
Bibliographic record
Abstract
BACKGROUND: The increase in the number of cancer cases and the evolution of cancer care management have become a significant problem for the French health care system, thereby making patient empowerment as a long sought-after goal in chronic pathologies. The implementation of an activation measure via the Patient Activation Measure-13 item (PAM-13) in the course of cancer care can potentially highlight the patient's needs, with nursing care adapting accordingly. OBJECTIVE: The objectives of this PARACT (PARAmedical Interventions on Patient ACTivation) multicentric study were as follows: (1) evaluate the implementation of PAM-13 in oncology nursing practices in 5 comprehensive cancer centers, (2) identify the obstacles and facilitators to the implementation of PAM-13, and (3) produce recommendations for the dissemination of such interventions in other comprehensive cancer centers. METHODS: This study will follow the "Reach, Effectiveness, Adoption, Implementation, and Maintenance" framework and will consist of 3 stages. First, a robust preimplementation analysis will be conducted using the Theoretical Domains Framework (TDF) linked to the "Capability, Opportunity, Motivation, and Behavior" model to identify the obstacles and facilitators to implementing new nursing practices in each context. Then, using the Behavior Change Wheel, we will personalize a strategy for implementing the PAM-13, depending on the specificities of each context, to encourage acceptability by the nursing staff involved in the project. This analysis will be performed via a qualitative study through semistructured interviews. Second, the patient will be included in the study for 12 months, during which the patient care pathway will be studied, particularly to collect all relevant contacts of oncology nurses and other health professionals involved in the pathway. The axes of nursing care will also be collected. The primary goal is to implement PAM-13. Secondary factors to be measured are the patient's anxiety level, quality of life, and health literacy level. The oncology nurses will be responsible for completing the questionnaires when the patient is at the hospital for his/her intravenous chemotherapy/immunotherapy treatment. The questionnaires will be completed thrice in a year: (1) at the time of the patient's enrollment, (2) at 6 months, and (3) at 12 months. Third, a postimplementation analysis will be performed through semistructured interviews using the TDF to investigate the implementation problems at each site. RESULTS: This study was supported by a grant from the French Ministry of Health (PHRIP PARACT 2016-0405) and the Lucien Neuwirth Institute of Cancerology of Saint-Etienne, France. Data collection for this study is ongoing. CONCLUSIONS: This study would improve the implemented targeted nursing interventions in cancer centers so that a patient is offered a personalized cancer care pathway. Furthermore, measuring the level of activation and the implementation of measures intended to increase such activation could constitute a significant advantage in reducing social health inequalities. TRIAL REGISTRATION: ClinicalTrials.gov NCT03240341; https://clinicaltrials.gov/ct2/show/NCT03240341. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17485.
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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 | 0.046 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".