Add SAM question IDs.

exploration/test_JCQ_reversal.py

@@ -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"]

features/esm_COPE.py

@@ -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,

features/esm_JCQ.py

@@ -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,
+    Reverse JCQ demand and control answers.
-    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.
+    This function recodes answers in Job content questionnaire
-    These answers are listed in dict_JCQ_demand_control_reverse and identified by their question ID.
+        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
+        # Increment the original answer by 1 to keep in line
-        # to keep in line with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
+        # 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

features/esm_SAM.py

@@ -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.