A qualitative study of clinicians' impressions regarding the implementation of a clinical and research data collection system
Bibliographic record
Abstract
Abstract Background Systematically collecting data on clientele databases allows for describing the clientele's needs and addressing various clinical research questions. Conducted in a university psychology clinic, this initiative seeks to improve the overall quality of care provided by integrating evidence‐based practices. This study works to bridge the gap between clinical practice and research. Aims This study aimed to describe the potential repercussions and clinicians' impressions regarding the implementation of this procedure. Research Design An inductive qualitative approach, inspired by Husserl's descriptive phenomenology, was used. To be consistent with this approach, data analysis followed Giorgi's five‐step scientific phenomenological method. Data Collection and Analysis Semi‐structured individual interviews were conducted with 14 volunteer clinicians using an interview guide. The data were analysed to extract central themes using Giorgi's method, which involves collecting verbal data, reading the data, dividing it into units of meaning, organising the data using the language of the discipline and synthesising the results. Results Five central themes emerged: barriers to implementation, potential impacts on therapy, recommendations to enhance participation, perceived utility and an attitude of openness. Discussion The pilot project provides valuable insights into the feasibility and acceptability of systematic data collection in a clinical setting. Clinician consultation proves to be essential in the implementation process, highlighting the importance of addressing practical and philosophical obstacles. Conclusion Understanding clinicians' experiences can guide future implementations of similar systems and improve clinical practice by supporting the integration of systematic data collection. Enhanced communication and training on the data collection system are suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".