The Utility of Researcher-Driven Projective and Enabling Techniques to Support Engagement in Research About Dementia Diagnosis and Post-Diagnostic Support
Bibliographic record
Abstract
Research involving people with dementia has highlighted the need to improve engagement in the conduct of interviews and focus groups. Projective and enabling techniques may be useful and avoid some of the drawbacks associated with direct questioning. However, researcher-driven projective techniques have not been extensively tested in research with people with dementia. In 2019, researchers in Australia, Canada, the Netherlands and Poland received training and trialled projective and enabling techniques to collect data about dementia diagnostic and early post-diagnostic experiences. The techniques were used with a total of thirty people with dementia (aged 67–97 years) in online and face-to-face individual and dyadic interviews and a focus group. Word association activities supported brainstorming about the concepts of ‘dementia’ and ‘support’. A researcher-driven photo elicitation technique was utilised to seek responses concerning a hypothetical couple at four time points: during a diagnostic conversation, and at 1, 6 and 12-month post-diagnosis. Discussions were audio recorded and transcribed and interviewers created ‘meta’ mind maps of word associations and made reflective notes regarding participant engagement. Deductive content analysis was used to assess the value of the techniques to support a manageable, comprehensible and meaningful research experience. Word associations supported free-flowing conversations around the key research concepts. Photo elicitation techniques promoted empathy and supported personal reflections on the probable experiences and needs of the hypothetical couple. The techniques were also useful in eliciting reflections on personal experiences, societal responses to dementia, and recommendations for improving the diagnostic conversation and supports for the post-diagnostic period. Overall, the techniques appeared to lessen some of the demands of direct questioning but were not manageable or meaningful for all participants. Further research should explore the vast array of projective techniques and engage in greater co-design and tailoring of research approaches to enhance the toolkit of dementia researchers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.251 | 0.300 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".