stress_at_work_analysis/features/esm_JCQ.py

import pandas as pd

JCQ_ORIGINAL_MAX = 4
JCQ_ORIGINAL_MIN = 1

dict_JCQ_demand_control_reverse = {
    75: (
        "I was NOT asked",
        "Men legde mij geen overdreven",
        "Men legde mij GEEN overdreven",  # Capitalized in some versions
        "Od mene se NI zahtevalo",
    ),
    76: (
        "I had enough time to do my work",
        "Ik had voldoende tijd om mijn werk",
        "Imela sem dovolj časa, da končam",
        "Imel sem dovolj časa, da končam",
    ),
    77: (
        "I was free of conflicting demands",
        "Er werden mij op het werk geen tegenstrijdige",
        "Er werden mij op het werk GEEN tegenstrijdige",  # Capitalized in some versions
        "Pri svojem delu se NISEM srečeval",
    ),
    79: (
        "My job involved a lot of repetitive work",
        "Mijn taak omvatte veel repetitief werk",
        "Moje delo je vključevalo veliko ponavljajočega",
    ),
    85: (
        "On my job, I had very little freedom",
        "In mijn taak had ik zeer weinig vrijheid",
        "Pri svojem delu sem imel zelo malo svobode",
        "Pri svojem delu sem imela zelo malo svobode",
    ),
}


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.
    However, the existing data is checked against literal phrasing of these questions
        to protect against wrong numbering of questions (differing question IDs).

    Parameters
    ----------
    df_esm_jcq_demand_control: pd.DataFrame
        A cleaned up dataframe, which must also include esm_user_answer_numeric.

    Returns
    -------
    df_esm_jcq_demand_control: pd.DataFrame
        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")
        .esm_instructions.value_counts()
        .rename()
        .reset_index()
    )
    # Tabulate all possible answers to each question (group by question ID).
    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,
            "esm_instructions",
        ]
        # 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)
        )
        # See if they are expected, i.e. included in the dictionary.
        if ~answers_matches.all():
            print("One of the answers that occur in the data should not be reversed.")
            print("This was the answer found in the data: ")
            raise KeyError(possible_answers[~answers_matches])
            # In case there is an unexpected answer, raise an exception.

    try:
        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).
        df_esm_jcq_demand_control[
            df_esm_jcq_demand_control["question_id"].isin(
                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()
            )
        ].assign(
            esm_user_score=lambda x: JCQ_ORIGINAL_MAX
            + JCQ_ORIGINAL_MIN
            - x.esm_user_score
        )
        # Reverse the items that require it.
    except AttributeError as e:
        print("Please, clean the dataframe first using features.esm.clean_up_esm.")
        print(e)

    return df_esm_jcq_demand_control