Engaging patients, family caregivers and healthcare providers to develop metrics tailored to a palliative care population: a content validity process
Bibliographic record
Abstract
BACKGROUND: Assessment of patient readiness for hospital discharge has been advocated as an important component of discharge preparation. However, no measures focused on hospital-to-home transitions for patients receiving a palliative approach to care, or the associated difficulties in coping at home after hospital discharge, have been developed to date. Using a co-design approach, the purpose of this study was to (1) adapt two scales to a palliative care population, one of which was developed to assess readiness for the hospital-to-home transition and another developed to assess difficulty in coping post-transition and to (2) test the content validity of both scales from the perspectives of patients, family caregivers, and healthcare providers. The scales chosen for adaptation were the Readiness for Hospital Discharge Scale and Post-Discharge Coping Difficulty Scale. METHODOLOGY: The research team made initial adaptations to scale language prior to developing three parallel versions of each scale to be patient-, family caregiver-, and healthcare provider-facing. We conducted content validity testing of the items on both scales by asking each participant group to rate scale items on their usefulness, and to provide suggestions on ways items could be improved. We calculated the Item Content Validity Index and a modified Kappa statistic for each scale item, and calculated the Scale Content Validity Index for each of the three versions of the scales. Refinements were informed by qualitative feedback provided by participants during the content validity process. Final refinements were informed by members of a Patient and Family Advisory Council, and healthcare provider research team members. RESULTS: Moderate modifications were required to the three versions of both scales. Modifications included adding items, modifying item language, and adding examples in parentheses to enhance item context. Patients, family caregivers, and healthcare providers deemed the research team's initial modifications to the scales useful, as evidenced by each scale yielding a Scale Content Validity Index of higher than 0.5. CONCLUSION: The methodology provided can be used as an example of ways to engage and leverage the experiences of healthcare system users and healthcare providers throughout the outcome measures development process. The next steps will be to utilize the adapted scales as intervention outcome measures in a subsequent implementation study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.392 | 0.532 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".