Slot: population_characteristics¶
How this cohort deviates from the typical, where no other field covers it: habitual exposure (heavy caffeine use, social drinkers, smoking status), training or expertise (athletes, musicians, bilinguals, gamers), occupation or social role (veterans, undergraduates, shift workers), lifestyle (sedentary, vegetarian, meditation practitioners), and body habitus where it is atypical (obese, overweight, underweight).
Deviation is the test, and it is what makes this field worth querying. A study selects its cohort for something; that something is what belongs here. The unremarkable does not: "normal weight", "healthy weight", "typically developing", "right-handed", "no hearing loss" and an ordinary age range describe a cohort that was not selected for anything, and recording them fills the field with the absence of information. If every plausible cohort could carry the value, leave it out.
Every other Group slot takes priority. Nothing here that belongs in age_mean, age_minimum, age_maximum, age_median, sex_distribution, gender_distribution, handedness_distribution, race_distribution, ethnicity_distribution, education_summary, socioeconomic_status_summary, medications, medication_status, recruitment_method, species, inclusion_criteria, exclusion_criteria or medical_condition. "All Caucasian" is race_distribution; "children aged 8-11" is age_minimum and age_maximum; "five participants were female" is sex_distribution.
Not a diagnosis either. Where a trait is also the cohort's condition -- alcohol dependence, obesity as a diagnosed disease -- medical_condition takes it alone, which is where a clinician would look.
What was screened out is not a characteristic. "Excluding caffeine" in an exclusion list says nothing about caffeine use in the cohort.
URI: neurostudy:population_characteristics
Applicable Classes¶
| Name | Description | Modifies Slot |
|---|---|---|
| Group | An aggregate participant group and its reported demographics | no |
Properties¶
Type and Range¶
| Property | Value |
|---|---|
| Range | String |
| Domain Of | Group |
| ### Cardinality and Requirements |
| Property | Value |
|---|---|
| Multivalued | Yes |
Slot Characteristics¶
| Property | Value |
|---|---|
| Owner | Group |
In Subsets¶
Identifier and Mapping Information¶
Schema Source¶
- from schema: https://neurostuff.org/schemas/neuroimaging-study-storage
Mappings¶
| Mapping Type | Mapped Value |
|---|---|
| self | neurostudy:population_characteristics |
| native | neurostudy:population_characteristics |
LinkML Source¶
name: population_characteristics
description: 'How this cohort **deviates from the typical**, where no other field
covers it: habitual exposure (heavy caffeine use, social drinkers, smoking status),
training or expertise (athletes, musicians, bilinguals, gamers), occupation or social
role (veterans, undergraduates, shift workers), lifestyle (sedentary, vegetarian,
meditation practitioners), and body habitus where it is atypical (obese, overweight,
underweight).
Deviation is the test, and it is what makes this field worth querying. A study selects
its cohort for something; that something is what belongs here. The unremarkable
does not: "normal weight", "healthy weight", "typically developing", "right-handed",
"no hearing loss" and an ordinary age range describe a cohort that was not selected
for anything, and recording them fills the field with the absence of information.
If every plausible cohort could carry the value, leave it out.
**Every other Group slot takes priority.** Nothing here that belongs in `age_mean`,
`age_minimum`, `age_maximum`, `age_median`, `sex_distribution`, `gender_distribution`,
`handedness_distribution`, `race_distribution`, `ethnicity_distribution`, `education_summary`,
`socioeconomic_status_summary`, `medications`, `medication_status`, `recruitment_method`,
`species`, `inclusion_criteria`, `exclusion_criteria` or `medical_condition`. "All
Caucasian" is `race_distribution`; "children aged 8-11" is `age_minimum` and `age_maximum`;
"five participants were female" is `sex_distribution`.
Not a diagnosis either. Where a trait is also the cohort''s condition -- alcohol
dependence, obesity as a diagnosed disease -- `medical_condition` takes it alone,
which is where a clinician would look.
What was screened out is not a characteristic. "Excluding caffeine" in an exclusion
list says nothing about caffeine use in the cohort.'
in_subset:
- mvp
- model_extracted
from_schema: https://neurostuff.org/schemas/neuroimaging-study-storage
rank: 1000
owner: Group
domain_of:
- Group
range: string
multivalued: true