Human Dignity Curriculum: Teachers’ comfort, commitment, and perceived support teaching a new socioemotional learning curriculum
Bibliographic record
Abstract
Globally, young people are experiencing unprecedented levels of socio-emotional loneliness, stress, and uncertainty. Formulating insight into their own and others’ experiences and behaviors is especially important during unsettling times and can be facilitated with socioemotional learning (SEL) curriculums. When implementing SEL programs, their success heavily depends upon teachers’ levels of commitment and comfort with the curriculum, as well as their perceived support from the administration; this is important and rarely studied. The current phenomenological qualitative study examined teachers’ experience during the early implementation of a new SEL, the Human Dignity Curriculum, (HDC) with middle school students in two small schools in Canada and the United States of America. Semi-structured interviews, using a four-question protocol developed for this study, were conducted with teachers who had completed teaching a 10-week module, as well as both school administrators. Written informed consent was obtained from each volunteer interviewee. Participants were asked, (1) Tell me about your experience while teaching HDC; (2) what was your commitment level to teaching HDC? (3) What was your comfort level with teaching HDC? (4) Did you feel supported by your administration when preparing and teaching HDC? Inductive thematic analysis identified five themes indicating HDC was a (1) user-friendly, (2) high value curriculum; (3) teachers felt supported teaching it; and a (4) shared language, (5) and shared teacher-student experience evolved when teaching HDC. Future research to replicate this initial evaluation of teachers’ experience and exploration of HDC’s potential impacts on youths’ well-being and related behavioral outcomes appears warranted.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".