Bibliographic record
Abstract
Background and Objective: Over the past two decades the total fertility rate has remained the below-replacement level (two children per woman), which can lead to aging population and hence shortage of labor forces required for the development of the country. This research aimed to study reasons behind the intention to have no children. Materials and Methods: This descriptive study uses data from the 2012 Tehran Survey of Fertility Intentions (n= 2267), conducted among married men and women under aged 36 living in 22 districts of the city of Tehran. The samples were selected by a two stage cluster random sampling method, and a structured questionnaire was used to collect data by interviewing respondents face-to-face at the door of their residence. Using SPSS version 24 and descriptive statistics, reasons for not wanting children were examined according to respondents’ socio-economic characteristics. Results: Overall, 40% (902) of respondents reported that they intend not to have any children in the future. Number of living children was negatively related to the intention to have children. About 9% (67) of childless respondents reported that they intend to remain childless forever, and 34% (302) of respondents with one child and 81% (443) of those having two children stated that they intend to stop childbearing. “Not being able to afford the cost of raising children” (27%), “having the desired number of children” (25%), “being worry about the future of their children” (15%) as the most important reasons for not wanting any (more) children. The other reason for intending not to have children was “conflict of childbearing with own personal life, plans and interests” (16%), which was reported largely by men, and those with higher levels of education and income, and those with one or no child. Respondents also reported “spouse’s opposition” 6%) and “problems in spousal relationships” (2%) as the other reasons for wanting no more children. Conclusion: The study showed that beyond of economic reasons, uncertainty about children’s future and childbearing-personal life conflict are two other important cultural and social reasons for low fertility intention, which are required to be considered by policy makers developing or tailoring the country’s population policies. Ethical code: Nipissing University Research Ethics Board File number: 12-06-11. DOI: http://doi.org/10.22037/ch.v6i2.22583.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.125 | 0.101 |
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".