Canadian Oncology Nursing Journal, Vol. 11, no. 3 (summer 2001)
Bibliographic record
Abstract
Make-A-WishThe Make-A-Wish Foundation is the largest and most respected wish granting organization in the world.With chapters in 22 countries, Make-A-Wish has granted over 80,000 wishes worldwide to children between the ages of three and 18 years old diagnosed with a life threatening illness.A wish offers a child who is sick -laughter, strength, and hope during a very difficult time.For more information, please visit www.makeawish.ca or call 1-888-822-WISH. Common mistakes in trying to apply qualitative methodsThe rigorous application of research methods is crucial to the outcome of qualitative studies.Two common mistakes are the incomplete application of a method, seen in studies in which interview data are coded but the conceptual and theoretical work is not completed, and attempts to apply qualitative methods to textual data obtained as part of a quantitative study.Without the conceptual and theoretical phases of the analytic qualitative research process, the reader is left with a "so what?" feeling.The potential contribution to the development of nursing knowledge is lost.Attempts to apply qualitative methods to data obtained from samples in which the theoretical sampling requirements of the method were violated seriously compromise data quality and, thus, threaten the validity of the study findings.Qualitative research methods all require data obtained by articulate informants who are carefully selected on the basis of their ability to explain their views.The data are collected in this way so that they are sufficiently rich to permit the type of analysis required.It is very unlikely that this kind of data could be obtained, for example, from open-ended questions included in a survey distributed to a random sample.Given these brief descriptions above, it is clear that each qualitative method serves a different purpose.In order for these purposes to be achieved and for the results of the study to contribute to nursing knowledge, the research question must match the qualitative method selected and the particular rules of the selected method must be followed carefully and in their entirety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.174 | 0.019 |
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".