liu_2023_adherence_tools
708 responses from 236 respondents to 3 items.
About this table
| Description | 3-item adherence-assessment-tools battery (1-5), same sample as liu_2023_training_freq, n=236 |
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| Reference | Liu et al (2023). Hypertension doctors' awareness and practice of medication adherence in hypertensive patients: a questionnaire-based survey. PeerJ. |
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| DOI | 10.7717/peerj.16384 |
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| Licence | CC BY 4.0 |
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| Source data | https://europepmc.org/article/PMC/PMC10693237 |
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Size and shape
| Responses | 708 |
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| Respondents | 236 |
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| Items | 3 |
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| Response categories | 5 |
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| Responses per respondent | 3 |
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| Responses per item | 236 |
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| Density | 1 |
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| Longitudinal | FALSE |
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Classification
| sample | Workplace |
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| measurement tool | Survey/questionnaire |
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| item format | Likert Scale/selected response |
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| primary language(s) | chi |
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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), Q13: awareness of medication-adherence assessment tools |
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| Mean words per item | 4 |
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| Mean characters per item | 30 |
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| Mean characters per response | 3.800 |
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| Flesch-Kincaid grade level | 13.331 |
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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_adherence_tools")
# Python
pip install irw
import irw
df = irw.fetch("liu_2023_adherence_tools")
Version and provenance
| IRW version | v393 |
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| Redivis dataset | item_response_warehouse_4 v7.0 |
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| Redivis dataset DOI | 10.57761/cpj5-vm97 |
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| Manifest pin for this IRW version | v7.0 |
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| Metadata source | irw_meta v23.0 |
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