Overcoming Loss: Activities and Stories to Help Transform Children's Grief and Loss
Bibliographic record
Abstract
Introduction. How to use this workbook. PART I: Expressive activities : Feelings. Activity 1 - Creating a safe place to be...: An introduction to feeling work. Activity 2 - Feeling puzzle: What are the names of your feelings? Activity 3 - Match the feeling game: Recognizing your feelings. Activity 4 - Faces collage: Learning what emotions are expressed on faces. Activity 5 - Feeling faces: Draw a face to match the feeling word. Activity 6 - Feeling words: Building a vocabulary of feeling words. Activity 7 - Finish the sentence...: Choose a feeling word from the vocabulary list to end these sentences. Activity 8 - What color are my feelings? Find out what color your feelings are. Activity 9 - Feelings rainbow: Expressing your feelings through colors. PART II: Expressive activities: Identifying everyday losses. Activity 10 - Saying goodbye is hard to do: Learning about everyday closures. Activity 11 - Talking about losses of pets or toys: Recognizing, talking about, and drawing pet or toy losses. Activity 12 - Good memories: Gathering memories. Activity 13 - Memory boxes: A place to honor. Activity 14 - New perspectives: A new way to remember and feel. PART III: Approaching the loss experience through fiction. Activity 15 - Story time: Lilly has to say goodbye PART IV: Creating Groups: Four-week curriculum. Tools and templates: Appendix A: Cover page to be used if exercises are to be made into a personalized book. Appendix B: Feelings vocabulary list. Appendix C: US, Canadian, UK and EU resources international web resources. Appendix D: Recommended reading. Appendix E: Handouts A1-A4. Appendix F: Group screening questionnaire and group evaluation activity. About the author.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.165 | 0.075 |
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".