FACTORS ASSOCIATED WITH PARENT SATISFACTION OF SCHOOL BASED MENTAL HEALTH SERVICES
Bibliographic record
Abstract
It is important to understand the significant factors that predict parent satisfaction of school based mental health services. These factors are vital as they can decrease attrition rates regarding treatment and can improve the overall quality of services in school based settings. The purpose of this study is to examine the predictors of parent satisfaction by utilizing secondary data acquired from the Ontario-Montclair School District. The measurement instrument utilized was a 10 question survey that included 8 Likert-style questions and one free response question. This study utilized an independent samples t-test which found that school type and language had no significant impact on overall parent satisfaction. This study also utilized a Pearson r correlation coefficient and found that length of sessions, number of sessions, information given, contact with counselor, behavioral outcome and timeliness all have significant positive relationships with overall satisfaction. Lastly, a multiple linear regression was utilized and found that the three strongest predictors of overall satisfaction were behavioral outcome, timeliness of services and information given. Findings from the study can provide social work practitioners with information on how to improve their service delivery in a school setting. Recommendations for future research are also discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".