Sense of coherence among unemployed nurses
Bibliographic record
Abstract
AIMS: This paper reports a study assessing Finnish unemployed nurses' sense of coherence and the factors relating to it. BACKGROUND: During the 1990s, due to the widespread economic downturn in Finland, the nursing profession suffered from a high level of unemployment. Previous research has clearly indicated that unemployment is detrimental to health. It creates stress by disturbing a person's sense of identity and self-esteem and by disrupting social networks. In Finland, many studies have been conducted on the impact of unemployment, but have not examined the sense of coherence of unemployed nurses. METHODS: Data were collected in one Employment and Economic Development Centre area in Finland in 1998. Structured questionnaires were used to collect data among Finnish unemployed nurses (n = 183), and included the General Health Questionnaire, measuring nurses' mental health; socio-demographic questions; and the 13-item version of the Sense of Coherence scale based on Antonovsky's salutogenic model to measure sense of coherence. RESULTS: Although the majority of unemployed nurses had a strong sense of coherence, many felt that during their period of unemployed they did not feel at ease, did not know what to do and had a sense of being unfairly treated. Daily household chores, on the whole, were perceived as meaningful. Income and the state of mental health were positively correlated with nurses' sense of coherence: the better the family income and state of mental health, the stronger was their sense of coherence. Because of the low response rate (less than 50%), the results might be skewed by those whose higher sense of coherence made them more motivated to complete the questionnaires. CONCLUSIONS: Many of the nurses reported low sense of coherence and poor general health. Special interventions should be designed to improve their sense of coherence and high motivation level, and to maintain their professional competence when they return to work. This kind of support may prevent further out-migration and nursing shortages from Finland and other industrialized countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".