liu_2023_poor_adherence
1,416 responses from 236 respondents to 6 items.
About this table
| Description | 6-item binary poor-adherence-cause checklist, same sample as liu_2023_training_freq, n=236 |
|---|
| Reference | Liu et al (2023). Hypertension doctors' awareness and practice of medication adherence in hypertensive patients: a questionnaire-based survey. PeerJ. |
|---|
| DOI | 10.7717/peerj.16384 |
|---|
| Licence | CC BY 4.0 |
|---|
| Source data | https://europepmc.org/article/PMC/PMC10693237 |
|---|
Size and shape
| Responses | 1,416 |
|---|
| Respondents | 236 |
|---|
| Items | 6 |
|---|
| Response categories | 2 |
|---|
| Responses per respondent | 6 |
|---|
| Responses per item | 236 |
|---|
| Density | 1 |
|---|
| Longitudinal | FALSE |
|---|
Classification
| sample | Workplace |
|---|
| measurement tool | Survey/questionnaire |
|---|
| item format | Likert Scale/selected response |
|---|
| primary language(s) | chi |
|---|
Item text
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Hypertension doctors' medication adherence questionnaire (Liu et al., 2023), Q16 poor-adherence situations checklist |
|---|
| Mean words per item | 4 |
|---|
| Mean characters per item | 29.500 |
|---|
| Mean characters per response | 3 |
|---|
| Flesch-Kincaid grade level | 12.520 |
|---|
Get the data
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("liu_2023_poor_adherence")
# Python
pip install irw
import irw
df = irw.fetch("liu_2023_poor_adherence")
Version and provenance
| IRW version | v393 |
|---|
| Redivis dataset | item_response_warehouse_4 v7.0 |
|---|
| Redivis dataset DOI | 10.57761/cpj5-vm97 |
|---|
| Manifest pin for this IRW version | v7.0 |
|---|
| Metadata source | irw_meta v23.0 |
|---|