Single mother families : a participant observation study of human service agencies, organizations / by Melissa Reynolds.
Bibliographic record
Abstract
The purpose of this mixed methods study was to better understand single mother families by integrating both qualitative data describing the lived experiences and needs of single mother families, with quantitative data detailing the formal services/programs currently available to meet these needs of single mother families living in Thunder Bay, Ontario, Canada. Qualitative analysis consisted of continual reflection and interpretation of day-to-day life experiences of 8 young single mother families through participant observations for a total of 180 hours of participant observation. In the quantitative approach, statistical analysis using SPSS 15.0 was completed on survey data from 30 human service agencies and organizations to learn about their extent of involvement with \nservices/programs for single mother families in Thunder Bay. Analysis of the participant observation data revealed that the single mothers demonstrated resourcefulness, dignity, caution, astuteness, maturity, adaptability, coping, and tenacity in maintaining daily family living, along with hopefulness towards their futures. Additionally, the single mothers often relied on support from family and friends to maintain and provide for their families. The results of the statistical analysis revealed that only 33 percent of the agencies/organizations provided special services/programs for single mother families and 70 percent of these agencies/organization provided regular programs in which single mother families are eligible to participate. From the perspective of the experiences of the single mother families it was revealed that the needs of single mother families living in Thunder Bay are not sufficiently met by the services/programs available.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".