Planning and Development: Social Capital and Promoting Mental Health
Bibliographic record
Abstract
During the planning and development of a country, social capital along with natural, human and physical capitals is considered as input and output of development. In underdeveloped countries, social capital is called as missing link development. According to the impact of this factor on varicose aspects of human life and more importantly on Mental Health, the present study attempted to identify the main factor of Mental Health and how to increase it by Social Capital and its dimensions (Cognitive, Relative and Structural Capitals). Present study researchers have used the Social capital and Mental Health theories, application survey and questionnaire. In present research the sample size consists of 264 employees (59 women and 205 men) that were selected at random from 243 small and medium enterprises located in Science and Technology Park. Data analysis was carried out by using the statistical program packages SPSS 17.0, AMOS SPSS 16.0.1 and LISREL 8.54. Results of the present study were illustrated that there is significant relationship between Social capital and its dimensions and Mental health in the present companies (p<0/01). The results of Enter Regression showed that predictor variables significantly (cognitive, relative and structural capital) have determined 47.7 % of the variance of Mental Health together. Also the result of LISREL statistical software was illustrated that the data of present study involve significant goodness of fit. Also the interesting results were obtained from Regression analysis and Factor Analysis to predict Social capital and its dimensions on the mental health that will watch in the present study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".