Personal Construct Theory: a strategy for the study of multidimensional phenomena in nursing.
Bibliographic record
Abstract
Nursing research is characterized by the study of complex phenomena relative to health behaviours, health-care services, illness, and hospitalization events.The challenge for researchers is to accurately capture and analyze multidimensional phenomena in the context of a dynamic interplay of events and interactions in clinical settings.Traditional methodologies measure a variety of concepts using strategies such as observation and questionnaires that rely on descriptive and inferential statistical analysis. However, the dynamic interplay of experiences, perceptions, and meanings in the social context of the clinical setting is more difficult to examine using these methodologies. In a recent study, an innovative, multidimensional theory and accompanying methodology were employed to examine parents’ experiences in the dynamic social context of the hospital setting. Personal Construct Theory, with its accompanying methodology, Repertory Grid Technique, is a new approach (based on an old theory) to nursing research that is especially well suited to the study of complex, multidimensional research questions. Implications for nursing research, theory development, and practice will be examined relative to the utility of Personal Construct Theory.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".