Gender differences in experiencing negative encounters with healthcare: A study of long-term sickness absentees
Bibliographic record
Abstract
AIM: In most countries there are gender differences in sickness absence and in absentees' return to work (RTW). According to different theories sick-listed persons' experiences of encounters with healthcare professionals can influence self-esteem and RTW. The aim was to analyse gender differences in sickness absentees' experiences of negative encounters with healthcare professionals. METHODS: A questionnaire, comprising numerous questions on experiences of positive and negative encounters with professionals, was constructed and sent to 10,100 individuals who had been on sick leave for the last 6-8 months. The response rate was 58% (n = 5,802). RESULTS: Almost one-third (32%) of the female respondents and one-quarter of the male (24%), respectively, had experienced negative encounters. The most common of such experiences among both women and men were: that they were treated with indifference, with disrespect, that the professional did not take his/her time, did not listen, did not believe in, or doubted complaints. In regression analyses the women had higher significant crude odds ratios, ranging from 1.29 to 1.71, for agreeing to the separate statements on negative encounters. When adjusting for age, ethnicity, and level of education the gender differences were still significant for 14 of the 23 the statements. CONCLUSION: Women's high rate of sickness absence is considered a problem in most countries. The subjective experiences of women are an important factor to consider in efforts aiming at reducing the sick-leave rates. One important endeavour among professionals in healthcare could be to shift the focus towards a more empowering professional role.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".