A comparative cross-sectional study was conducted; it took 6 months from January to June 2023.

Forty-eight medical and 48 non-medical female workers aged 18–45 years from Zagazig University were included following a simple random technique. The medical workers were from the faculty of medicine and university hospitals. Non-medical workers were a matched group from the same university. Our exclusion criteria were pregnancy, a known psychiatric illness or gynecological problem that may mimic or exacerbate symptoms of PMS, hysterectomy, or being on hormonal treatments, including oral contraceptive pills.

The open Epi version 6 statistics program was used to calculate the sample size under the following assumptions: The confidence interval was 95%, the precision degree was 80%, and the prevalence of PMS was 80.2% among medical professionals and 36% among non-medical professionals [6]. As a result, our sample was 48 workers in each group (medical and non-medical).

We collected data through self-reported questionnaires from both groups. The questionnaire consisted of 3 parts. The first covered sociodemographic and occupational data like duration of employment, number of working hours per week, and work pattern (set hourly schedule or non-set hourly schedule). Medical workers were asked about their specialty, and whether their work was academic teaching or clinical practice.

The second part was the Premenstrual Symptoms Screening Tool (PSST) [11], a 19-item questionnaire with two domains: the first domain comprises the 14 DSM-IV physical and psychological manifestations of PMS/PMDD, while the second domain is composed of five items assessing the functional impact of premenstrual symptoms. Each item is rated on a 4-point Likert scale from “not at all” to “severe”. Diagnosis of PMDD (severe form) requires: (1) the presence of at least five symptoms from the first domain, rated as moderate to severe; (2) at least one of the first four symptoms (anger/irritability; anxiety/tension; tearful/increased sensitivity to rejections; and depressed mood/hopelessness) must be rated as severe; and (3) severe functional impact, at least one item of the second domain rated as severe. Moderate PMS diagnosis is established by the following criteria: (1) at least five of the premenstrual symptoms of the first domain rated as moderate to severe; (2) the presence of at least one of the first four symptoms rated as moderate or severe; and (3) at least one item of the second domain rated as moderate or severe. Participants who do not fulfill any of these three criteria are classified as no/mild PMS [11].

The third part was the Work-Related Quality of Life Scale (WRQL) [12], the second edition was used (added as a supplementary file). It contains 32 questions and seven subscales, including job and career satisfaction (JCS) (questions 1, 3, 8, 11, 18, and 20), working conditions (WCS) (questions 13, 16, 22, and 31), general well-being (GWB) (questions 4, 9, 10, 15, 17, and 21), home-work interface (HWI) (questions 5, 6, and 14), stress at work (SAW) (questions 7, 19, 24, and 29), control at work (CAW) (questions 2, 12, 23, and 30), and employee engagement (EEN) (questions 26, 27, and 28). The overall quality of life associated with work was examined in Question 32. Responses were collected according to a Likert scale from 1 ‘strongly disagree’ to 5 ‘strongly agree’ Before determining the subscale scores, the negative items were reverse coded. The total score ranged from 32 to 160 with higher scores showing a better work-related quality of life. Workers were considered to have a good WRQL when they scored above the median of 80, and a bad WRQL when they scored below 80.

To anticipate potential data collection challenges and establish the required time for data collection, a pilot study with 10 participants (10% of the total study participants) was carried out. Following the pilot, no changes were deemed necessary, so the pilot sample was added to the main sample.

The SPSS program version 25.0 [13] was used to analyze the gathered data. Using the Chi-square (χ2) test, qualitative data were compared and presented as frequencies and percentages. Binary logistic regression was used to examine potential PMS and WRQL predictors. The independent factors were working as a medical worker, specialty, duration of employment, working hours per week, shifts per month, non-set hourly work schedule, and severity of PMS. The link between PMS and the WRQL subscale scores was investigated using the Spearman correlation analysis. P values below 0.05 were deemed statistically significant, whereas values < 0.001 were deemed highly significant.