Calculate JCQ control and demand control ratio.
Include norms and corresponding quartile.labels
junos
parent
2fed962644
commit
b5a6317f4b
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@ -1,3 +1,4 @@
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import numpy as np
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import pandas as pd
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pid = snakemake.params["pid"]
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@ -23,6 +24,26 @@ dict_JCQ_demand_control_reverse = {
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LIMESURVEY_JCQ_MIN = 1
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LIMESURVEY_JCQ_MAX = 4
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DEMAND_CONTROL_RATIO_MIN = 5 / (9 * 4)
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DEMAND_CONTROL_RATIO_MAX = (4 * 5) / 9
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JCQ_NORMS = {
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"F": {
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0: DEMAND_CONTROL_RATIO_MIN,
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1: 0.45,
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2: 0.52,
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3: 0.62,
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4: DEMAND_CONTROL_RATIO_MAX,
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},
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"M": {
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0: DEMAND_CONTROL_RATIO_MIN,
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1: 0.41,
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2: 0.48,
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3: 0.56,
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4: DEMAND_CONTROL_RATIO_MAX,
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},
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}
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participant_info = pd.read_csv(snakemake.input[0], parse_dates=["date_of_birth"])
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if not participant_info.empty:
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@ -37,38 +58,117 @@ if not participant_info.empty:
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baseline_features.loc[0, "startlanguage"] = participant_info.loc[
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0, "startlanguage"
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]
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if "demand" in requested_features:
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if (
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("demand" in requested_features)
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or ("control" in requested_features)
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or ("demand_control_ratio" in requested_features)
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):
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participant_info_t = participant_info.T
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rows_baseline = participant_info_t.index
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if ("demand" in requested_features) or (
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"demand_control_ratio" in requested_features
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):
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# Find questions about demand, but disregard time (duration of filling in questionnaire)
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rows_demand = rows_baseline.str.startswith(
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JCQ_DEMAND
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) & ~rows_baseline.str.endswith("Time")
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limesurvey_control = (
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limesurvey_demand = (
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participant_info_t[rows_demand]
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.reset_index()
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.rename(columns={"index": "question", 0: "score_original"})
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)
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# Extract question IDs from names such as JobEisen[3]
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limesurvey_demand.loc[:, "qid"] = (
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limesurvey_demand["question"].str.extract(r"\[(\d+)\]").astype(int)
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)
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limesurvey_demand["score"] = limesurvey_demand["score_original"]
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# Identify rows that include questions to be reversed.
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rows_demand_reverse = limesurvey_demand["qid"].isin(
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dict_JCQ_demand_control_reverse[JCQ_DEMAND].keys()
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)
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# Reverse the score, so that the maximum value becomes the minimum etc.
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limesurvey_demand.loc[rows_demand_reverse, "score"] = (
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LIMESURVEY_JCQ_MAX
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+ LIMESURVEY_JCQ_MIN
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- limesurvey_demand.loc[rows_demand_reverse, "score_original"]
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)
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# TODO Write to data/interim
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if "demand" in requested_features:
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baseline_features.loc[0, "limesurvey_demand"] = limesurvey_demand[
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"score"
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].sum()
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if ("control" in requested_features) or (
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"demand_control_ratio" in requested_features
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):
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# Find questions about control, but disregard time (duration of filling in questionnaire)
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rows_control = rows_baseline.str.startswith(
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JCQ_CONTROL
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) & ~rows_baseline.str.endswith("Time")
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limesurvey_control = (
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participant_info_t[rows_control]
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.reset_index()
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.rename(columns={"index": "question", 0: "score_original"})
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)
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# Extract question IDs from names such as JobControle[3]
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limesurvey_control.loc[:, "qid"] = (
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limesurvey_control["question"].str.extract(r"\[(\d+)\]").astype(int)
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)
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limesurvey_control["score"] = limesurvey_control["score_original"]
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# Identify rows that include questions to be reversed.
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rows_demand_reverse = limesurvey_control["qid"].isin(
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dict_JCQ_demand_control_reverse[JCQ_DEMAND].keys()
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rows_control_reverse = limesurvey_control["qid"].isin(
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dict_JCQ_demand_control_reverse[JCQ_CONTROL].keys()
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)
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# Reverse the score, so that the maximum value becomes the minimum etc.
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limesurvey_control.loc[rows_demand_reverse, "score"] = (
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limesurvey_control.loc[rows_control_reverse, "score"] = (
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LIMESURVEY_JCQ_MAX
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+ LIMESURVEY_JCQ_MIN
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- limesurvey_control.loc[rows_demand_reverse, "score_original"]
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- limesurvey_control.loc[rows_control_reverse, "score_original"]
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)
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# TODO Write to data/interim
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baseline_features.loc[0, "limesurvey_demand"] = limesurvey_control[
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if "control" in requested_features:
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baseline_features.loc[0, "limesurvey_control"] = limesurvey_control[
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"score"
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].sum()
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if "demand_control_ratio" in requested_features:
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limesurvey_demand_control_ratio = (
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limesurvey_demand["score"].sum() / limesurvey_control["score"].sum()
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)
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if (
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JCQ_NORMS[participant_info.loc[0, "gender"]][0]
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<= limesurvey_demand_control_ratio
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< JCQ_NORMS[participant_info.loc[0, "gender"]][1]
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):
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limesurvey_quartile = 1
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elif (
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JCQ_NORMS[participant_info.loc[0, "gender"]][1]
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<= limesurvey_demand_control_ratio
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< JCQ_NORMS[participant_info.loc[0, "gender"]][2]
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):
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limesurvey_quartile = 2
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elif (
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JCQ_NORMS[participant_info.loc[0, "gender"]][2]
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<= limesurvey_demand_control_ratio
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< JCQ_NORMS[participant_info.loc[0, "gender"]][3]
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):
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limesurvey_quartile = 3
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elif (
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JCQ_NORMS[participant_info.loc[0, "gender"]][3]
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<= limesurvey_demand_control_ratio
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< JCQ_NORMS[participant_info.loc[0, "gender"]][4]
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):
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limesurvey_quartile = 4
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else:
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limesurvey_quartile = np.nan
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baseline_features.loc[
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0, "limesurvey_demand_control_ratio"
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] = limesurvey_demand_control_ratio
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baseline_features.loc[
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0, "limesurvey_demand_control_ratio_quartile"
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] = limesurvey_quartile
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baseline_features.to_csv(
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snakemake.output[0], index=False, encoding="utf-8",
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