Amateur Family Genealogists Researching Their Family History: A Scoping Review of Motivations and Psychosocial Impacts
Bibliographic record
Abstract
A rapidly rising number of people are engaging in family genealogical research and have purchased home-based DNA testing kits due to increased access to online resources and consumer products. The purpose of this systematic scoping review is to identify and elucidate the motivations (i.e., pathways, reasons for conducting family history research) and the consequences (i.e., psychosocial impacts) of participating in this activity by amateur (unpaid) family genealogists. Studies published from January 2000 to June 2023 were included in our review, using the PRISMA methodology outlined by the Joanna Briggs Institute’s (JBI) Reviewer Manual. A total of 1986 studies were identified using selected keywords and electronic databases. A full-text review was conducted of 73 studies, 26 of which met our eligibility criteria. The multiple dominant themes that emerged from the data analysis are organized into five categories: (1) the motivations for practicing family history research, (2) emotional responses to family secrets and previously unknown truths, (3) impacts on relationship with the family of origin and other relatives, (4) impacts on personal identity (including ethnic/racialized and family/social), and (5) identity exploration and reconstruction. Finally, these themes are connected to broader theoretical/conceptual linkages, and further, an agenda for future research inquiry is developed.
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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 | 0.050 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".