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stress_at_work_analysis
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Add SAM question IDs.

master
junos 2023-07-03 20:38:20 +02:00
parent 64c05ec5ec
commit ef26772038
4 changed files with 167 additions and 18 deletions
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4
exploration/test_JCQ_reversal.py
View File

@ -15,7 +15,7 @@
# %%
import pandas as pd
from features.esm_JCQ import dict_JCQ_demand_control_reverse
from features.esm_JCQ import DICT_JCQ_DEMAND_CONTROL_REVERSE
# %%
limesurvey_questions = pd.read_csv(
@ -115,7 +115,7 @@ limesurvey_control["qid"][0]
# %%
rows_demand_reverse = limesurvey_control["qid"].isin(
dict_JCQ_demand_control_reverse.keys()
DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()
)
limesurvey_control.loc[rows_demand_reverse, "score"] = (
4 + 1 - limesurvey_control.loc[rows_demand_reverse, "score_original"]

8
features/esm_COPE.py
View File

@ -3,7 +3,7 @@ import pandas as pd
COPE_ORIGINAL_MAX = 4
COPE_ORIGINAL_MIN = 1
dict_COPE_question_ids = {
DICT_COPE_QUESTION_IDS = {
164: (
"I took additional action to try to get rid of the problem",
"Ik deed extra mijn best om er iets aan te doen",
@ -137,7 +137,7 @@ def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
# Tabulate all possible answers to each question (group by question ID).
# First, check that we anticipated all esm instructions.
for q_id in dict_COPE_question_ids.keys():
for q_id in DICT_COPE_QUESTION_IDS.keys():
# Look for all questions ("instructions") occurring in the dataframe.
actual_questions = df_esm_cope_unique_questions.loc[
df_esm_cope_unique_questions["question_id"] == q_id,
@ -145,7 +145,7 @@ def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
]
# These are all answers to a given question (by q_id).
questions_matches = actual_questions.str.startswith(
dict_COPE_question_ids.get(q_id)
DICT_COPE_QUESTION_IDS.get(q_id)
)
# See if they are expected, i.e. included in the dictionary.
if ~actual_questions.all():
@ -160,7 +160,7 @@ def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
lambda x: next(
(
key
for key, values in dict_COPE_question_ids.items()
for key, values in DICT_COPE_QUESTION_IDS.items()
if x.startswith(values)
),
None,

29
features/esm_JCQ.py
View File

@ -3,7 +3,7 @@ import pandas as pd
JCQ_ORIGINAL_MAX = 4
JCQ_ORIGINAL_MIN = 1
dict_JCQ_demand_control_reverse = {
DICT_JCQ_DEMAND_CONTROL_REVERSE = {
75: (
"I was NOT asked",
"Men legde mij geen overdreven",
@ -40,10 +40,14 @@ def reverse_jcq_demand_control_scoring(
df_esm_jcq_demand_control: pd.DataFrame,
) -> pd.DataFrame:
"""
This function recodes answers in Job content questionnaire by first incrementing them by 1,
to be in line with original (1-4) scoring.
Then, some answers are reversed (i.e. 1 becomes 4 etc.), because the questions are negatively phrased.
These answers are listed in dict_JCQ_demand_control_reverse and identified by their question ID.
Reverse JCQ demand and control answers.
This function recodes answers in Job content questionnaire
by first incrementing them by 1, to be in line with original (1-4) scoring.
Then, some answers are reversed (i.e. 1 becomes 4 etc.),
because the questions are negatively phrased.
These answers are listed in DICT_JCQ_DEMAND_CONTROL_REVERSE
and identified by their question ID.
However, the existing data is checked against literal phrasing of these questions
to protect against wrong numbering of questions (differing question IDs).
@ -55,7 +59,8 @@ def reverse_jcq_demand_control_scoring(
Returns
-------
df_esm_jcq_demand_control: pd.DataFrame
The same dataframe with a column esm_user_score containing answers recoded and reversed.
The same dataframe with a column esm_user_score
containing answers recoded and reversed.
"""
df_esm_jcq_demand_control_unique_answers = (
df_esm_jcq_demand_control.groupby("question_id")
@ -64,7 +69,7 @@ def reverse_jcq_demand_control_scoring(
.reset_index()
)
# Tabulate all possible answers to each question (group by question ID).
for q_id in dict_JCQ_demand_control_reverse.keys():
for q_id in DICT_JCQ_DEMAND_CONTROL_REVERSE.keys():
# Look through all answers that need to be reversed.
possible_answers = df_esm_jcq_demand_control_unique_answers.loc[
df_esm_jcq_demand_control_unique_answers["question_id"] == q_id,
@ -72,7 +77,7 @@ def reverse_jcq_demand_control_scoring(
]
# These are all answers to a given question (by q_id).
answers_matches = possible_answers.str.startswith(
dict_JCQ_demand_control_reverse.get(q_id)
DICT_JCQ_DEMAND_CONTROL_REVERSE.get(q_id)
)
# See if they are expected, i.e. included in the dictionary.
if ~answers_matches.all():
@ -85,15 +90,15 @@ def reverse_jcq_demand_control_scoring(
df_esm_jcq_demand_control = df_esm_jcq_demand_control.assign(
esm_user_score=lambda x: x.esm_user_answer_numeric + 1
)
# Increment the original answer by 1
# to keep in line with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
# Increment the original answer by 1 to keep in line
# with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
df_esm_jcq_demand_control[
df_esm_jcq_demand_control["question_id"].isin(
dict_JCQ_demand_control_reverse.keys()
DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()
)
] = df_esm_jcq_demand_control[
df_esm_jcq_demand_control["question_id"].isin(
dict_JCQ_demand_control_reverse.keys()
DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()
)
].assign(
esm_user_score=lambda x: JCQ_ORIGINAL_MAX

144
features/esm_SAM.py
View File

@ -24,6 +24,150 @@ GROUP_QUESTIONNAIRES_BY = [
# within the same participant.
DICT_SAM_QUESTION_IDS = {
87: (
"Was there a particular event that created tension in you?",
"Was er een bepaalde gebeurtenis die spanning veroorzaakte?",
"Je prišlo do kakega dogodka, ki je v vas ustvaril napetost?",
),
88: (
"Did this event make you feel anxious?",
"Voelde je je angstig door deze gebeurtenis?",
"Ste se zaradi tega dogodka počutili tesnobno?",
),
89: (
"Will the outcome of this event be negative?",
"Zal de uitkomst van deze gebeurtenis negatief zijn? ",
"Bo izid tega dogodka negativen?",
),
90: (
"How threatening was this event?",
"Hoe bedreigend was deze gebeurtenis?",
"Kako grozeč je bil ta dogodek?",
),
91: (
"Is this going to have a negative impact on you?",
"Zal dit een negatieve impact op je hebben?",
"Ali bo to negativno vplivalo na vas?",
),
92: (
"Is this going to have a positive impact on you?",
"Zal dit een positief effect op je hebben?",
"Ali bo to pozitivno vplivalo na vas?",
),
93: (
"How eager are you to tackle this event?",
"Hoe graag wil je deze gebeurtenis aanpakken?",
"Kako zagnani ste bili pri spopadanju s tem dogodkom?",
),
94: (
"To what extent can you become a stronger person because of this event?",
"In welke mate kan je een sterkere persoon worden door deze gebeurtenis?",
"V kolikšni meri lahko zaradi tega dogodka postanete močnejša oseba?",
),
95: (
"To what extent are you excited thinking about the outcome of this event?",
"In welke mate ben je enthousiast bij de gedachte aan",
"V kolikšni meri vas misel na izid tega dogodka navdušuje?",
),
96: (
"At what time did this event occur?",
"Hoe laat vond deze gebeurtenis plaats?",
"Kdaj se je ta dogodek zgodil?",
),
97: (
"How long did this event last?",
"Hoe lang duurde deze gebeurtenis?",
"Kako dolgo je trajal ta dogodek?",
),
98: (
"Was/is this event work-related?",
"Was/is deze gebeurtenis werkgerelateerd?",
"Je (bil) ta dogodek povezan s službo?",
),
99: (
"Did this overall period create tension in you?",
"Heeft deze globale periode spanning veroorzaakt?",
"Je to obdobje kot celota v vas ustvarilo napetost?",
),
100: (
"To what extent do you perceive this overall period as stressful?",
"In welke mate ervaar je deze globale periode als stressvol?",
"V kolikšni meri ste to obdobje dojemali kot stresno?",
),
101: (
"Was there a particular event that created tension in you?",
"Was er een bepaalde gebeurtenis die spanning veroorzaakte?",
"Je prišlo do kakega dogodka, ki je v vas ustvaril napetost?",
),
102: (
"Did this event make you feel anxious?",
"Voelde je je angstig door deze gebeurtenis?",
"Ste se zaradi tega dogodka počutili tesnobne?",
),
103: (
"Will the outcome of this event be negative?",
"Zal de uitkomst van deze gebeurtenis negatief zijn? ",
"Bo izid tega dogodka negativen?",
),
104: (
"How threatening was this event?",
"Hoe bedreigend was deze gebeurtenis?",
"Kako grozeč je bil ta dogodek?",
),
105: (
"Is this going to have a negative impact on you?",
"Zal dit een negatieve impact op je hebben?",
"Ali bo to negativno vplivalo na vas?",
),
106: (
"Is this going to have a positive impact on you?",
"Zal dit een positief effect op je hebben?",
"Ali bo to pozitivno vplivalo na vas?",
),
107: (
"How eager are you to tackle this event?",
"Hoe graag wil je deze gebeurtenis aanpakken?",
"Kako zagnani ste bili, da se spopadete s tem dogodkom?",
),
108: (
"To what extent can you become a stronger person because of this event?",
"In welke mate kan je een sterkere persoon worden door deze gebeurtenis?",
"V kolikšni meri lahko zaradi tega dogodka postanete močnejša oseba?",
),
109: (
"To what extent are you excited thinking about the outcome of this event?",
"In welke mate ben je enthousiast bij de gedachte",
"V kolikšni meri vas misel na izid tega dogodka navdušuje?",
),
110: (
"At what time did this event occur?",
"Hoe laat vond deze gebeurtenis plaats?",
"Kdaj se je ta dogodek zgodil?",
),
111: (
"How long did this event last?",
"Hoe lang duurde deze gebeurtenis?",
"Kako dolgo je trajal ta dogodek?",
),
112: (
"Was/is this event work-related?",
"Was/is deze gebeurtenis werkgerelateerd?",
"Je bil ali je ta dogodek povezan s službo?",
),
113: (
"Did this overall period create tension in you?",
"Heeft deze globale periode spanning veroorzaakt?",
"Je to celo obdobje v vas ustvarilo napetost?",
),
114: (
"To what extent do you perceive this overall period as stressful?",
"In welke mate ervaar je deze globale periode als stressvol?",
"V kolikšni meri ste celo to obdobje dojemali kot stresno?",
),
}
def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
"""
Extract information about stressful events.