Lone parents, health, wellbeing and welfare to work: a systematic review of qualitative studies
Bibliographic record
Abstract
BACKGROUND: Lone parents and their children experience higher than average levels of adverse health and social outcomes, much of which are explained by high rates of poverty. Many high income countries have attempted to address high poverty rates by introducing employment requirements for lone parents in receipt of welfare benefits. However, there is evidence that employment may not reduce poverty or improve the health of lone parents and their children. METHODS: We conducted a systematic review of qualitative studies reporting lone parents' accounts of participation in welfare to work (WtW), to identify explanations and possible mechanisms for the impacts of WtW on health and wellbeing. Twenty one bibliographic databases were searched. Two reviewers independently screened references and assessed study quality. Studies from any high income country that met the criteria of focussing on lone parents, mandatory WtW interventions, and health or wellbeing were included. Thematic synthesis was used to investigate analytic themes between studies. RESULTS: Screening of the 4703 identified papers and quality assessment resulted in the inclusion of 16 qualitative studies of WtW in five high income countries, USA, Canada, UK, Australia, and New Zealand, covering a variety of welfare regimes. Our synthesis found that WtW requirements often conflicted with child care responsibilities. Available employment was often poorly paid and precarious. Adverse health impacts, such as increased stress, fatigue, and depression were commonly reported, though employment and appropriate training was linked to increased self-worth for some. WtW appeared to influence health through the pathways of conflict and control, analytical themes which emerged during synthesis. WtW reduced control over the nature of employment and care of children. Access to social support allowed some lone parents to manage the conflict associated with employment, and to increase control over their circumstances, with potentially beneficial health impacts. CONCLUSION: WtW can result in increased conflict and reduced control, which may lead to negative impacts on mental health. Availability of social support may mediate the negative health impacts of WtW.
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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 | 0.056 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".