Adult Projective Drawings in Pandemic Times: Draw-a-Person and Kinetic Family-Drawings with Associations
Bibliographic record
Abstract
This projective assessment for studying perceptions of self and family during COVID-19 was part of a project examining the adult decision-making process during the pandemic. For this research project, 110 adults aged 18-82 from the United States and Canada completed a background questionnaire and 30 pandemic-related questionnaire items. In addition, 84 participants (76%) each uploaded four projective drawings, with their accompanying written associations, of self and family as envisioned both prior to and during the pandemic, for a total of 336 drawings collected. These drawings represented Self Pre-Pandemic (SP); Self During Pandemic (SD); Family Pre-pandemic (FP) and Family During Pandemic (FD). Two coders rated each drawing with the accompanying associations for a) type of drawing: (stick figures; full figures; abstract/object-only/ non-peopled; faces only) b) figures depicted with masks c) drawings expressing affect d) activities possible before or during the pandemic e) relationship experiences and f) body image representations. Coder reliabilities were over 90% agreement. Happy faces and positive associations were dramatically more frequent for drawings and associations before versus during the pandemic, whereas ubiquitous expressions of isolation and negative affect were associated with SD and FD drawings. Examples of representative drawings with concomitant associations, for each of the four conditions (SP; FP; SD; FD) are presented, illustrated and discussed. Overall, we found that detailed, evocative expressions of affect were most prevalent in response to the projective drawings in contrast with questionnaire items, suggesting their utility for exploring experiences for persons affected by the pandemic and similar events.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".