Impact of age, sex and surgery type on engagement with an online patient education and support platform developed for total hip and knee replacement patients
Bibliographic record
Abstract
BACKGROUND: Patients should be active participants in the management of their condition and provided with appropriate information throughout their care pathway. We piloted an online digital platform (ODP) to deliver patient education and support (PES) for patients undergoing total hip (THR) and knee replacements (TKR). The aim of this study is to analyse the demographics of patients using the ODP, determine how and when they accessed the ODP and how different sexes and age groups interacted with the ODP. METHODS: Demographics and program library logs for patients registered to the ODP between 21st September 2017 and 28th May 2020 was obtained. Associations between age, sex, type of surgery and engagement were assessed using statistical comparisons. RESULTS: 1195 patients were registered on the ODP of which 832 (69.6%) accessed their carepacs. Patients accessed the content within their carepacs a mean of 29.1 times, spending a mean total time of 83 minutes. There was greater engagement for patients with a THR carepac (75.5%) compared to TKR (63.8%) (p<0.001). There were no differences in the proportion of patients that accessed the ODP or the total time spent on the platform dependent upon age (p = 0.34). Females accessed the platform more than males (p = 0.03). The use of a computer to access the ODP increased as age increased, whereas the use of a phone was favoured by the younger age groups (p<0.001). CONCLUSION: An ODP providing information to patients regarding their surgery is effective and demonstrates high levels of patient engagement. An online resource such as this does not discriminate against age or sex in terms of accessibility and can be useful for information provision.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".